Instructions and logic to perform floating point and integer operations for machine learning

ABSTRACT

One embodiment provides a graphics processor comprising a memory controller and a graphics processing resource coupled with the memory controller. The graphics processing resource includes circuitry configured to execute an instruction to perform a matrix operation on first input including weight data and second input including input activation data, generate intermediate data based on a result of the matrix operation, quantize the intermediate data to a floating-point format determined based on a statistical distribution of first output data, and output, as second output data, quantized intermediate data in a determined floating-point format.

CROSS-REFERENCE

This application is a continuation claiming priority to U.S. applicationSer. No. 17/305,355, filed Jul. 6, 2021, which claims priority to U.S.application Ser. No. 17/169,232, filed Feb. 5, 2021, now issued as U.S.Pat. No. 11,080,046, which claims priority to U.S. application Ser. No.17/115,989, filed Dec. 9, 2020, which claims priority to U.S.application Ser. No. 16/432,402, filed Jun. 5, 2019, now issued as U.S.Pat. No. 11,169,799, which claims priority to U.S. Pat. No. 10,353,706,issued on Jul. 16, 2019, which claims priority to U.S. Pat. No.10,474,458, issued on Nov. 12, 2019 which claims priority to U.S.Provisional Patent Application No. 62/491,699 filed Apr. 28, 2017, whichis hereby incorporated herein by reference.

FIELD

Embodiments relate generally to data processing and more particularly todata processing via a general-purpose graphics-processing unit.

BACKGROUND OF THE DESCRIPTION

Current parallel graphics data processing includes systems and methodsdeveloped to perform specific operations on graphics data such as, forexample, linear interpolation, tessellation, rasterization, texturemapping, depth testing, etc. Traditionally, graphics processors usedfixed function computational units to process graphics data; however,more recently, portions of graphics processors have been madeprogrammable, enabling such processors to support a wider variety ofoperations for processing vertex and fragment data.

To further increase performance, graphics processors typically implementprocessing techniques such as pipelining that attempt to process, inparallel, as much graphics data as possible throughout the differentparts of the graphics pipeline. Parallel graphics processors with singleinstruction, multiple thread (KNIT) architectures are designed tomaximize the amount of parallel processing in the graphics pipeline. Inan SIMT architecture, groups of parallel threads attempt to executeprogram instructions synchronously together as often as possible toincrease processing efficiency. A general overview of software andhardware for SIMT architectures can be found in Shane Cook, CUDAProgramming, Chapter 3, pages 37-51 (2013) and/or Nicholas Wilt, CUDAHandbook, A Comprehensive Guide to GPU Programming, Sections 2.6.2 to3.1.2 (June 2013).

BRIEF DESCRIPTION OF THE DRAWINGS

So that the features of the present invention can be understood indetail, a more particular description of the invention may be had byreference to embodiments, some of which are illustrated in the appendeddrawings. It is to be noted, however, that the appended drawingsillustrate only typical embodiments and are therefore not to beconsidered limiting of the scope of all embodiments.

FIG. 1 is a block diagram illustrating a computer system configured toimplement one or more aspects of the embodiments described herein;

FIG. 2A-2D illustrate parallel processor components, according to anembodiment;

FIG. 3A-3B are block diagrams of graphics multiprocessors, according toembodiments;

FIG. 4A-4F illustrate an exemplary architecture in which a plurality ofGPUs are communicatively coupled to a plurality of multi-coreprocessors;

FIG. 5 illustrates a graphics processing pipeline, according to anembodiment;

FIG. 6 illustrates a machine learning software stack, according to anembodiment;

FIG. 7 illustrates a highly-parallel general-purpose graphics processingunit, according to an embodiment;

FIG. 8 illustrates a multi-GPU computing system, according to anembodiment;

FIG. 9A-9B illustrate layers of exemplary deep neural networks;

FIG. 10 illustrates an exemplary recurrent neural network;

FIG. 11 illustrates training and deployment of a deep neural network;

FIG. 12 is a block diagram illustrating distributed learning;

FIG. 13 illustrates an exemplary inferencing system on a chip (SOC)suitable for performing inferencing using a trained model;

FIG. 14 is a block diagram of a multiprocessor unit, according to anembodiment;

FIG. 15A-15B illustrate designs for logic units to perform integer andfloating-point fused multiply-add operations, according to anembodiment;

FIG. 16 illustrates fused multiply-add logic unit having a mergedfloating-point and integer datapath, according to an embodiment;

FIG. 17A-17B illustrates logic units including merged computationcircuits to perform floating point and integer fused-multiply accumulateoperations, according to an embodiment;

FIG. 18A-18B illustrate a data processing system and associated computeand logic units that to perform accelerated training and inferencingoperations for machine learning;

FIG. 19 illustrates details of the activation instruction module,according to an embodiment;

FIG. 20 illustrates the stochastic quantization unit, according to anembodiment;

FIG. 21 illustrates an FPU encoding and configuration module, accordingto one embodiment;

FIG. 22 illustrates logic to process an instruction using a dynamicallyconfigurable compute unit, according to an embodiment;

FIG. 23A-23B are flow diagrams illustrating logic to perform sparsecompute operations within a GPGPU provided by embodiments describedherein;

FIG. 24 is a block diagram of a processing system, according to anembodiment;

FIG. 25 is a block diagram of a processor according to an embodiment;

FIG. 26 is a block diagram of a graphics processor, according to anembodiment;

FIG. 27 is a block diagram of a graphics processing engine of a graphicsprocessor in accordance with some embodiments;

FIG. 28 is a block diagram of a graphics processor provided by anadditional embodiment;

FIG. 29 illustrates thread execution logic including an array ofprocessing elements employed in some embodiments;

FIG. 30 is a block diagram illustrating graphics processor instructionformats according to some embodiments;

FIG. 31 is a block diagram of a graphics processor according to anotherembodiment.

FIG. 32A-32B illustrate a graphics processor command format and commandsequence, according to some embodiments;

FIG. 33 illustrates exemplary graphics software architecture for a dataprocessing system according to some embodiments;

FIG. 34 is a block diagram illustrating an IP core development system,according to an embodiment;

FIG. 35 is a block diagram illustrating an exemplary system on a chipintegrated circuit, according to an embodiment;

FIG. 36 is a block diagram illustrating an additional graphicsprocessor, according to an embodiment; and

FIG. 37 is a block diagram illustrating an additional exemplary graphicsprocessor of a system on a chip integrated circuit, according to anembodiment.

DETAILED DESCRIPTION

In some embodiments, a graphics processing unit (GPU) is communicativelycoupled to host/processor cores to accelerate graphics operations,machine-learning operations, pattern analysis operations, and variousgeneral-purpose GPU (GPGPU) functions. The GPU may be communicativelycoupled to the host processor/cores over a bus or another interconnect(e.g., a high-speed interconnect such as PCIe or NVLink). In otherembodiments, the GPU may be integrated on the same package or chip asthe cores and communicatively coupled to the cores over an internalprocessor bus/interconnect (i.e., internal to the package or chip).Regardless of the manner in which the GPU is connected, the processorcores may allocate work to the GPU in the form of sequences ofcommands/instructions contained in a work descriptor. The GPU then usesdedicated circuitry/logic for efficiently processing thesecommands/instructions.

Embodiments described herein provide an apparatus comprising amultiprocessor having a multithreaded architecture. The multiprocessorcan execute at least one single instruction to perform parallel mixedprecision matrix operations. In one embodiment the apparatus includes amemory interface and an array of multiprocessors coupled to the memoryinterface. At least one multiprocessor in the array of multiprocessorsis configured to execute a fused multiply-add instruction in parallelacross multiple threads.

In the following description, numerous specific details are set forth toprovide a more thorough understanding. However, be apparent to one ofskill in the art that the embodiments described herein may be practicedwithout one or more of these specific details. In other instances,well-known features have not been described to avoid obscuring thedetails of the present embodiments.

System Overview

FIG. 1 is a block diagram illustrating a computing system 100 configuredto implement one or more aspects of the embodiments described herein.The computing system 100 includes a processing subsystem 101 having oneor more processor(s) 102 and a system memory 104 communicating via aninterconnection path that may include a memory hub 105. The memory hub105 may be a separate component within a chipset component or may beintegrated within the one or more processor(s) 102. The memory hub 105couples with an I/O subsystem 111 via a communication link 106. The I/Osubsystem 111 includes an I/O hub 107 that can enable the computingsystem 100 to receive input from one or more input device(s) 108.Additionally, the I/O hub 107 can enable a display controller, which maybe included in the one or more processor(s) 102, to provide outputs toone or more display device(s) 110A. In one embodiment the one or moredisplay device(s) 110A coupled with the I/O hub 107 can include a local,internal, or embedded display device.

In one embodiment the processing subsystem 101 includes one or moreparallel processor(s) 112 coupled to memory hub 105 via a bus or othercommunication link 113. The communication link 113 may be one of anynumber of standards based communication link technologies or protocols,such as, but not limited to PCI Express, or may be a vendor specificcommunications interface or communications fabric. In one embodiment theone or more parallel processor(s) 112 form a computationally focusedparallel or vector processing system that can include a large number ofprocessing cores and/or processing clusters, such as a many integratedcore (MIC) processor. In one embodiment the one or more parallelprocessor(s) 112 form a graphics processing subsystem that can outputpixels to one of the one or more display device(s) 110A coupled via theI/O hub 107. The one or more parallel processor(s) 112 can also includea display controller and display interface (not shown) to enable adirect connection to one or more display device(s) 110B.

Within the I/O subsystem 111, a system storage unit 114 can connect tothe I/O hub 107 to provide a storage mechanism for the computing system100. An I/O switch 116 can be used to provide an interface mechanism toenable connections between the I/O hub 107 and other components, such asa network adapter 118 and/or wireless network adapter 119 that may beintegrated into the platform, and various other devices that can beadded via one or more add-in device(s) 120. The network adapter 118 canbe an Ethernet adapter or another wired network adapter. The wirelessnetwork adapter 119 can include one or more of a Wi-Fi, Bluetooth, nearfield communication (NFC), or other network device that includes one ormore wireless radios.

The computing system 100 can include other components not explicitlyshown, including USB or other port connections, optical storage drives,video capture devices, and the like, may also be connected to the I/Ohub 107. Communication paths interconnecting the various components inFIG. 1 may be implemented using any suitable protocols, such as PCI(Peripheral Component Interconnect) based protocols (e.g., PCI-Express),or any other bus or point-to-point communication interfaces and/orprotocol(s), such as the NV-Link high-speed interconnect, orinterconnect protocols known in the art.

In one embodiment, the one or more parallel processor(s) 112 incorporatecircuitry optimized for graphics and video processing, including, forexample, video output circuitry, and constitutes a graphics processingunit (GPU). In another embodiment, the one or more parallel processor(s)112 incorporate circuitry optimized for general purpose processing,while preserving the underlying computational architecture, described ingreater detail herein. In yet another embodiment, components of thecomputing system 100 may be integrated with one or more other systemelements on a single integrated circuit. For example, the one or moreparallel processor(s) 112, memory hub 105, processor(s) 102, and I/O hub107 can be integrated into a system on chip (SoC) integrated circuit.Alternatively, the components of the computing system 100 can beintegrated into a single package to form a system in package (SIP)configuration. In one embodiment at least a portion of the components ofthe computing system 100 can be integrated into a multi-chip module(MCM), which can be interconnected with other multi-chip modules into amodular computing system.

It will be appreciated that the computing system 100 shown herein isillustrative and that variations and modifications are possible. Theconnection topology, including the number and arrangement of bridges,the number of processor(s) 102, and the number of parallel processor(s)112, may be modified as desired. For instance, in some embodiments,system memory 104 is connected to the processor(s) 102 directly ratherthan through a bridge, while other devices communicate with systemmemory 104 via the memory hub 105 and the processor(s) 102. In otheralternative topologies, the parallel processor(s) 112 are connected tothe I/O hub 107 or directly to one of the one or more processor(s) 102,rather than to the memory hub 105. In other embodiments, the I/O hub 107and memory hub 105 may be integrated into a single chip. Someembodiments may include two or more sets of processor(s) 102 attachedvia multiple sockets, which can couple with two or more instances of theparallel processor(s) 112.

Some of the particular components shown herein are optional and may notbe included in all implementations of the computing system 100. Forexample, any number of add-in cards or peripherals may be supported, orsome components may be eliminated. Furthermore, some architectures mayuse different terminology for components similar to those illustrated inFIG. 1. For example, the memory hub 105 may be referred to as aNorthbridge in some architectures, while the I/O hub 107 may be referredto as a Southbridge.

FIG. 2A illustrates a parallel processor 200, according to anembodiment. The various components of the parallel processor 200 may beimplemented using one or more integrated circuit devices, such asprogrammable processors, application specific integrated circuits(ASICs), or field programmable gate arrays (FPGA). The illustratedparallel processor 200 is a variant of the one or more parallelprocessor(s) 112 shown in FIG. 1, according to an embodiment.

In one embodiment the parallel processor 200 includes a parallelprocessing unit 202. The parallel processing unit includes an I/O unit204 that enables communication with other devices, including otherinstances of the parallel processing unit 202. The I/O unit 204 may bedirectly connected to other devices. In one embodiment the I/O unit 204connects with other devices via the use of a hub or switch interface,such as memory hub 105. The connections between the memory hub 105 andthe I/O unit 204 form a communication link 113. Within the parallelprocessing unit 202, the I/O unit 204 connects with a host interface 206and a memory crossbar 216, where the host interface 206 receivescommands directed to performing processing operations and the memorycrossbar 216 receives commands directed to performing memory operations.

When the host interface 206 receives a command buffer via the I/O unit204, the host interface 206 can direct work operations to perform thosecommands to a front end 208. In one embodiment the front end 208 coupleswith a scheduler 210, which is configured to distribute commands orother work items to a processing cluster array 212. In one embodimentthe scheduler 210 ensures that the processing cluster array 212 isproperly configured and in a valid state before tasks are distributed tothe processing clusters of the processing cluster array 212. In oneembodiment the scheduler 210 is implemented via firmware logic executingon a microcontroller. The microcontroller implemented scheduler 210 isconfigurable to perform complex scheduling and work distributionoperations at coarse and fine granularity, enabling rapid preemption andcontext switching of threads executing on the processing cluster array212. In one embodiment, the host software can prove workloads forscheduling on the processing cluster array 212 via one of multiplegraphics processing doorbells. The workloads can then be automaticallydistributed across the processing cluster array 212 by the scheduler 210logic within the scheduler microcontroller.

The processing cluster array 212 can include up to “N” processingclusters (e.g., cluster 214A, cluster 214B, through cluster 214N). Eachcluster 214A-214N of the processing cluster array 212 can execute alarge number of concurrent threads. The scheduler 210 can allocate workto the clusters 214A-214N of the processing cluster array 212 usingvarious scheduling and/or work distribution algorithms, which may varydepending on the workload arising for each type of program orcomputation. The scheduling can be handled dynamically by the scheduler210, or can be assisted in part by compiler logic during compilation ofprogram logic configured for execution by the processing cluster array212. In one embodiment, different clusters 214A-214N of the processingcluster array 212 can be allocated for processing different types ofprograms or for performing different types of computations.

The processing cluster array 212 can be configured to perform varioustypes of parallel processing operations. In one embodiment theprocessing cluster array 212 is configured to perform general-purposeparallel compute operations. For example, the processing cluster array212 can include logic to execute processing tasks including filtering ofvideo and/or audio data, performing modeling operations, includingphysics operations, and performing data transformations.

In one embodiment the processing cluster array 212 is configured toperform parallel graphics processing operations. In embodiments in whichthe parallel processor 200 is configured to perform graphics processingoperations, the processing cluster array 212 can include additionallogic to support the execution of such graphics processing operations,including, but not limited to texture sampling logic to perform textureoperations, as well as tessellation logic and other vertex processinglogic. Additionally, the processing cluster array 212 can be configuredto execute graphics processing related shader programs such as, but notlimited to vertex shaders, tessellation shaders, geometry shaders, andpixel shaders. The parallel processing unit 202 can transfer data fromsystem memory via the I/O unit 204 for processing. During processing thetransferred data can be stored to on-chip memory (e.g., parallelprocessor memory 222) during processing, then written back to systemmemory.

In one embodiment, when the parallel processing unit 202 is used toperform graphics processing, the scheduler 210 can be configured todivide the processing workload into approximately equal sized tasks, tobetter enable distribution of the graphics processing operations tomultiple clusters 214A-214N of the processing cluster array 212. In someembodiments, portions of the processing cluster array 212 can beconfigured to perform different types of processing. For example a firstportion may be configured to perform vertex shading and topologygeneration, a second portion may be configured to perform tessellationand geometry shading, and a third portion may be configured to performpixel shading or other screen space operations, to produce a renderedimage for display. Intermediate data produced by one or more of theclusters 214A-214N may be stored in buffers to allow the intermediatedata to be transmitted between clusters 214A-214N for furtherprocessing.

During operation, the processing cluster array 212 can receiveprocessing tasks to be executed via the scheduler 210, which receivescommands defining processing tasks from front end 208. For graphicsprocessing operations, processing tasks can include indices of data tobe processed, e.g., surface (patch) data, primitive data, vertex data,and/or pixel data, as well as state parameters and commands defining howthe data is to be processed (e.g., what program is to be executed). Thescheduler 210 may be configured to fetch the indices corresponding tothe tasks or may receive the indices from the front end 208. The frontend 208 can be configured to ensure the processing cluster array 212 isconfigured to a valid state before the workload specified by incomingcommand buffers (e.g., batch-buffers, push buffers, etc.) is initiated.

Each of the one or more instances of the parallel processing unit 202can couple with parallel processor memory 222. The parallel processormemory 222 can be accessed via the memory crossbar 216, which canreceive memory requests from the processing cluster array 212 as well asthe I/O unit 204. The memory crossbar 216 can access the parallelprocessor memory 222 via a memory interface 218. The memory interface218 can include multiple partition units (e.g., partition unit 220A,partition unit 220B, through partition unit 220N) that can each coupleto a portion (e.g., memory unit) of parallel processor memory 222. Inone implementation the number of partition units 220A-220N is configuredto be equal to the number of memory units, such that a first partitionunit 220A has a corresponding first memory unit 224A, a second partitionunit 220B has a corresponding memory unit 224B, and an Nth partitionunit 220N has a corresponding Nth memory unit 224N. In otherembodiments, the number of partition units 220A-220N may not be equal tothe number of memory devices.

In various embodiments, the memory units 224A-224N can include varioustypes of memory devices, including dynamic random access memory (DRAM)or graphics random access memory, such as synchronous graphics randomaccess memory (SGRAM), including graphics double data rate (GDDR)memory. In one embodiment, the memory units 224A-224N may also include3D stacked memory, including but not limited to high bandwidth memory(HBM). Persons skilled in the art will appreciate that the specificimplementation of the memory units 224A-224N can vary, and can beselected from one of various conventional designs. Render targets, suchas frame buffers or texture maps may be stored across the memory units224A-224N, allowing partition units 220A-220N to write portions of eachrender target in parallel to efficiently use the available bandwidth ofparallel processor memory 222. In some embodiments, a local instance ofthe parallel processor memory 222 may be excluded in favor of a unifiedmemory design that utilizes system memory in conjunction with localcache memory.

In one embodiment, any one of the clusters 214A-214N of the processingcluster array 212 can process data that will be written to any of thememory units 224A-224N within parallel processor memory 222. The memorycrossbar 216 can be configured to transfer the output of each cluster214A-214N to any partition unit 220A-220N or to another cluster214A-214N, which can perform additional processing operations on theoutput. Each cluster 214A-214N can communicate with the memory interface218 through the memory crossbar 216 to read from or write to variousexternal memory devices. In one embodiment the memory crossbar 216 has aconnection to the memory interface 218 to communicate with the I/O unit204, as well as a connection to a local instance of the parallelprocessor memory 222, enabling the processing units within the differentprocessing clusters 214A-214N to communicate with system memory or othermemory that is not local to the parallel processing unit 202. In oneembodiment the memory crossbar 216 can use virtual channels to separatetraffic streams between the clusters 214A-214N and the partition units220A-220N.

While a single instance of the parallel processing unit 202 isillustrated within the parallel processor 200, any number of instancesof the parallel processing unit 202 can be included. For example,multiple instances of the parallel processing unit 202 can be providedon a single add-in card, or multiple add-in cards can be interconnected.The different instances of the parallel processing unit 202 can beconfigured to inter-operate even if the different instances havedifferent numbers of processing cores, different amounts of localparallel processor memory, and/or other configuration differences. Forexample and in one embodiment, some instances of the parallel processingunit 202 can include higher precision floating point units relative toother instances. Systems incorporating one or more instances of theparallel processing unit 202 or the parallel processor 200 can beimplemented in a variety of configurations and form factors, includingbut not limited to desktop, laptop, or handheld personal computers,servers, workstations, game consoles, and/or embedded systems.

FIG. 2B is a block diagram of a partition unit 220, according to anembodiment. In one embodiment the partition unit 220 is an instance ofone of the partition units 220A-220N of FIG. 2A. As illustrated, thepartition unit 220 includes an L2 cache 221, a frame buffer interface225, and a ROP 226 (raster operations unit). The L2 cache 221 is aread/write cache that is configured to perform load and store operationsreceived from the memory crossbar 216 and ROP 226. Read misses andurgent write-back requests are output by L2 cache 221 to frame bufferinterface 225 for processing. Updates can also be sent to the framebuffer via the frame buffer interface 225 for processing. In oneembodiment the frame buffer interface 225 interfaces with one of thememory units in parallel processor memory, such as the memory units224A-224N of FIG. 2A (e.g., within parallel processor memory 222).

In graphics applications, the ROP 226 is a processing unit that performsraster operations such as stencil, z test, blending, and the like. TheROP 226 then outputs processed graphics data that is stored in graphicsmemory. In some embodiments the ROP 226 includes compression logic tocompress depth or color data that is written to memory and decompressdepth or color data that is read from memory. The compression logic canbe lossless compression logic that makes use of one or more of multiplecompression algorithms. The type of compression that is performed by theROP 226 can vary based on the statistical characteristics of the data tobe compressed. For example, in one embodiment, delta color compressionis performed on depth and color data on a per-tile basis.

In some embodiments, the ROP 226 is included within each processingcluster (e.g., cluster 214A-214N of FIG. 2A) instead of within thepartition unit 220. In such embodiment, read and write requests forpixel data are transmitted over the memory crossbar 216 instead of pixelfragment data. The processed graphics data may be displayed on a displaydevice, such as one of the one or more display device(s) 110 of FIG. 1,routed for further processing by the processor(s) 102, or routed forfurther processing by one of the processing entities within the parallelprocessor 200 of FIG. 2A.

FIG. 2C is a block diagram of a processing cluster 214 within a parallelprocessing unit, according to an embodiment. In one embodiment theprocessing cluster is an instance of one of the processing clusters214A-214N of FIG. 2A. The processing cluster 214 can be configured toexecute many threads in parallel, where the term “thread” refers to aninstance of a particular program executing on a particular set of inputdata. In some embodiments, single-instruction, multiple-data (SIMD)instruction issue techniques are used to support parallel execution of alarge number of threads without providing multiple independentinstruction units. In other embodiments, single-instruction,multiple-thread (SIMT) techniques are used to support parallel executionof a large number of generally synchronized threads, using a commoninstruction unit configured to issue instructions to a set of processingengines within each one of the processing clusters. Unlike a SIMDexecution regime, where all processing engines typically executeidentical instructions, SIMT execution allows different threads to morereadily follow divergent execution paths through a given thread program.Persons skilled in the art will understand that a SIMD processing regimerepresents a functional subset of a SIMT processing regime.

Operation of the processing cluster 214 can be controlled via a pipelinemanager 232 that distributes processing tasks to SIMT parallelprocessors. The pipeline manager 232 receives instructions from thescheduler 210 of FIG. 2A and manages execution of those instructions viaa graphics multiprocessor 234 and/or a texture unit 236. The illustratedgraphics multiprocessor 234 is an exemplary instance of a SIMT parallelprocessor. However, various types of SIMT parallel processors ofdiffering architectures may be included within the processing cluster214. One or more instances of the graphics multiprocessor 234 can beincluded within a processing cluster 214. The graphics multiprocessor234 can process data and a data crossbar 240 can be used to distributethe processed data to one of multiple possible destinations, includingother shader units. The pipeline manager 232 can facilitate thedistribution of processed data by specifying destinations for processeddata to be distributed via the data crossbar 240.

Each graphics multiprocessor 234 within the processing cluster 214 caninclude an identical set of functional execution logic (e.g., arithmeticlogic units, load-store units, etc.). The functional execution logic canbe configured in a pipelined manner in which new instructions can beissued before previous instructions are complete. The functionalexecution logic supports a variety of operations including integer andfloating point arithmetic, comparison operations, Boolean operations,bit-shifting, and computation of various algebraic functions. In oneembodiment the same functional-unit hardware can be leveraged to performdifferent operations and any combination of functional units may bepresent.

The instructions transmitted to the processing cluster 214 constitutes athread. A set of threads executing across the set of parallel processingengines is a thread group. A thread group executes the same program ondifferent input data. Each thread within a thread group can be assignedto a different processing engine within a graphics multiprocessor 234. Athread group may include fewer threads than the number of processingengines within the graphics multiprocessor 234. When a thread groupincludes fewer threads than the number of processing engines, one ormore of the processing engines may be idle during cycles in which thatthread group is being processed. A thread group may also include morethreads than the number of processing engines within the graphicsmultiprocessor 234. When the thread group includes more threads than thenumber of processing engines within the graphics multiprocessor 234,processing can be performed over consecutive clock cycles. In oneembodiment multiple thread groups can be executed concurrently on agraphics multiprocessor 234.

In one embodiment the graphics multiprocessor 234 includes an internalcache memory to perform load and store operations. In one embodiment,the graphics multiprocessor 234 can forego an internal cache and use acache memory (e.g., L1 cache 248) within the processing cluster 214.Each graphics multiprocessor 234 also has access to L2 caches within thepartition units (e.g., partition units 220A-220N of FIG. 2A) that areshared among all processing clusters 214 and may be used to transferdata between threads. The graphics multiprocessor 234 may also accessoff-chip global memory, which can include one or more of local parallelprocessor memory and/or system memory. Any memory external to theparallel processing unit 202 may be used as global memory. Embodimentsin which the processing cluster 214 includes multiple instances of thegraphics multiprocessor 234 can share common instructions and data,which may be stored in the L1 cache 248.

Each processing cluster 214 may include an MMU 245 (memory managementunit) that is configured to map virtual addresses into physicaladdresses. In other embodiments, one or more instances of the MMU 245may reside within the memory interface 218 of FIG. 2A. The MMU 245includes a set of page table entries (PTEs) used to map a virtualaddress to a physical address of a tile and optionally a cache lineindex. The MMU 245 may include address translation lookaside buffers(TLB) or caches that may reside within the graphics multiprocessor 234or the L1 cache or processing cluster 214. The physical address isprocessed to distribute surface data access locality to allow efficientrequest interleaving among partition units. The cache line index may beused to determine whether a request for a cache line is a hit or miss.

In graphics and computing applications, a processing cluster 214 may beconfigured such that each graphics multiprocessor 234 is coupled to atexture unit 236 for performing texture mapping operations, e.g.,determining texture sample positions, reading texture data, andfiltering the texture data. Texture data is read from an internaltexture L1 cache (not shown) or in some embodiments from the L1 cachewithin graphics multiprocessor 234 and is fetched from an L2 cache,local parallel processor memory, or system memory, as needed. Eachgraphics multiprocessor 234 outputs processed tasks to the data crossbar240 to provide the processed task to another processing cluster 214 forfurther processing or to store the processed task in an L2 cache, localparallel processor memory, or system memory via the memory crossbar 216.A preROP 242 (pre-raster operations unit) is configured to receive datafrom graphics multiprocessor 234, direct data to ROP units, which may belocated with partition units as described herein (e.g., partition units220A-220N of FIG. 2A). The preROP 242 unit can perform optimizations forcolor blending, organize pixel color data, and perform addresstranslations.

It will be appreciated that the core architecture described herein isillustrative and that variations and modifications are possible. Anynumber of processing units, e.g., graphics multiprocessor 234, textureunits 236, preROPs 242, etc., may be included within a processingcluster 214. Further, while only one processing cluster 214 is shown, aparallel processing unit as described herein may include any number ofinstances of the processing cluster 214. In one embodiment, eachprocessing cluster 214 can be configured to operate independently ofother processing clusters 214 using separate and distinct processingunits, L1 caches, etc.

FIG. 2D shows a graphics multiprocessor 234, according to oneembodiment. In such embodiment the graphics multiprocessor 234 coupleswith the pipeline manager 232 of the processing cluster 214. Thegraphics multiprocessor 234 has an execution pipeline including but notlimited to an instruction cache 252, an instruction unit 254, an addressmapping unit 256, a register file 258, one or more general purposegraphics processing unit (GPGPU) cores 262, and one or more load/storeunits 266. The GPGPU cores 262 and load/store units 266 are coupled withcache memory 272 and shared memory 270 via a memory and cacheinterconnect 268.

In one embodiment, the instruction cache 252 receives a stream ofinstructions to execute from the pipeline manager 232. The instructionsare cached in the instruction cache 252 and dispatched for execution bythe instruction unit 254. The instruction unit 254 can dispatchinstructions as thread groups (e.g., warps), with each thread of thethread group assigned to a different execution unit within GPGPU core262. An instruction can access any of a local, shared, or global addressspace by specifying an address within a unified address space. Theaddress mapping unit 256 can be used to translate addresses in theunified address space into a distinct memory address that can beaccessed by the load/store units 266.

The register file 258 provides a set of registers for the functionalunits of the graphics multiprocessor 234. The register file 258 providestemporary storage for operands connected to the data paths of thefunctional units (e.g., GPGPU cores 262, load/store units 266) of thegraphics multiprocessor 234. In one embodiment, the register file 258 isdivided between each of the functional units such that each functionalunit is allocated a dedicated portion of the register file 258. In oneembodiment, the register file 258 is divided between the different warpsbeing executed by the graphics multiprocessor 234.

The GPGPU cores 262 can each include floating point units (FPUs) and/orinteger arithmetic logic units (ALUs) that are used to executeinstructions of the graphics multiprocessor 234. The GPGPU cores 262 canbe similar in architecture or can differ in architecture, according toembodiments. For example and in one embodiment, a first portion of theGPGPU cores 262 include a single precision FPU and an integer ALU whilea second portion of the GPGPU cores include a double precision FPU. Inone embodiment the FPUs can implement the IEEE 754-2008 standard forfloating point arithmetic or enable variable precision floating pointarithmetic. The graphics multiprocessor 234 can additionally include oneor more fixed function or special function units to perform specificfunctions such as copy rectangle or pixel blending operations. In oneembodiment one or more of the GPGPU cores can also include fixed orspecial function logic.

In one embodiment the GPGPU cores 262 include SIMD logic capable ofperforming a single instruction on multiple sets of data. In oneembodiment GPGPU cores 262 can physically execute SIMD4, SIMD8, andSIMD16 instructions and logically execute SIMD1, SIMD2, and SIMD32instructions. The SIMD instructions for the GPGPU cores can be generatedat compile time by a shader compiler or automatically generated whenexecuting programs written and compiled for single program multiple data(SPMD) or SIMT architectures. Multiple threads of a program configuredfor the SIMT execution model can be executed via a single SIMDinstruction. For example and in one embodiment, eight SIMT threads thatperform the same or similar operations can be executed in parallel via asingle SIMD8 logic unit.

The memory and cache interconnect 268 is an interconnect network thatconnects each of the functional units of the graphics multiprocessor 234to the register file 258 and to the shared memory 270. In oneembodiment, the memory and cache interconnect 268 is a crossbarinterconnect that allows the load/store unit 266 to implement load andstore operations between the shared memory 270 and the register file258. The register file 258 can operate at the same frequency as theGPGPU cores 262, thus data transfer between the GPGPU cores 262 and theregister file 258 is very low latency. The shared memory 270 can be usedto enable communication between threads that execute on the functionalunits within the graphics multiprocessor 234. The cache memory 272 canbe used as a data cache for example, to cache texture data communicatedbetween the functional units and the texture unit 236. The shared memory270 can also be used as a program managed cached. Threads executing onthe GPGPU cores 262 can programmatically store data within the sharedmemory in addition to the automatically cached data that is storedwithin the cache memory 272.

FIG. 3A-3B illustrate additional graphics multiprocessors, according toembodiments. The illustrated graphics multiprocessors 325, 350 arevariants of the graphics multiprocessor 234 of FIG. 2C. The illustratedgraphics multiprocessors 325, 350 can be configured as a streamingmultiprocessor (SM) capable of simultaneous execution of a large numberof execution threads.

FIG. 3A shows a graphics multiprocessor 325 according to an additionalembodiment. The graphics multiprocessor 325 includes multiple additionalinstances of execution resource units relative to the graphicsmultiprocessor 234 of FIG. 2D. For example, the graphics multiprocessor325 can include multiple instances of the instruction unit 332A-332B,register file 334A-334B, and texture unit(s) 344A-344B. The graphicsmultiprocessor 325 also includes multiple sets of graphics or computeexecution units (e.g., GPGPU core 336A-336B, GPGPU core 337A-337B, GPGPUcore 338A-338B) and multiple sets of load/store units 340A-340B. In oneembodiment the execution resource units have a common instruction cache330, texture and/or data cache memory 342, and shared memory 346.

The various components can communicate via an interconnect fabric 327.In one embodiment the interconnect fabric 327 includes one or morecrossbar switches to enable communication between the various componentsof the graphics multiprocessor 325. In one embodiment the interconnectfabric 327 is a separate, high-speed network fabric layer upon whicheach component of the graphics multiprocessor 325 is stacked. Thecomponents of the graphics multiprocessor 325 communicate with remotecomponents via the interconnect fabric 327. For example, the GPGPU cores336A-336B, 337A-337B, and 3378A-338B can each communicate with sharedmemory 346 via the interconnect fabric 327. The interconnect fabric 327can arbitrate communication within the graphics multiprocessor 325 toensure a fair bandwidth allocation between components.

FIG. 3B shows a graphics multiprocessor 350 according to an additionalembodiment. The graphics processor includes multiple sets of executionresources 356A-356D, where each set of execution resource includesmultiple instruction units, register files, GPGPU cores, and load storeunits, as illustrated in FIG. 2D and FIG. 3A. The execution resources356A-356D can work in concert with texture unit(s) 360A-360D for textureoperations, while sharing an instruction cache 354, and shared memory362. In one embodiment the execution resources 356A-356D can share aninstruction cache 354 and shared memory 362, as well as multipleinstances of a texture and/or data cache memory 358A-358B. The variouscomponents can communicate via an interconnect fabric 352 similar to theinterconnect fabric 327 of FIG. 3A.

Persons skilled in the art will understand that the architecturedescribed in FIGS. 1, 2A-2D, and 3A-3B are descriptive and not limitingas to the scope of the present embodiments. Thus, the techniquesdescribed herein may be implemented on any properly configuredprocessing unit, including, without limitation, one or more mobileapplication processors, one or more desktop or server central processingunits (CPUs) including multi-core CPUs, one or more parallel processingunits, such as the parallel processing unit 202 of FIG. 2A, as well asone or more graphics processors or special purpose processing units,without departure from the scope of the embodiments described herein.

In some embodiments a parallel processor or GPGPU as described herein iscommunicatively coupled to host/processor cores to accelerate graphicsoperations, machine-learning operations, pattern analysis operations,and various general purpose GPU (GPGPU) functions. The GPU may becommunicatively coupled to the host processor/cores over a bus or otherinterconnect (e.g., a high speed interconnect such as PCIe or NVLink).In other embodiments, the GPU may be integrated on the same package orchip as the cores and communicatively coupled to the cores over aninternal processor bus/interconnect (i.e., internal to the package orchip). Regardless of the manner in which the GPU is connected, theprocessor cores may allocate work to the GPU in the form of sequences ofcommands/instructions contained in a work descriptor. The GPU then usesdedicated circuitry/logic for efficiently processing thesecommands/instructions.

Techniques for GPU to Host Processor Interconnection

FIG. 4A illustrates an exemplary architecture in which a plurality ofGPUs 410-413 are communicatively coupled to a plurality of multi-coreprocessors 405-406 over high-speed links 440A-440D (e.g., buses,point-to-point interconnects, etc.). In one embodiment, the high-speedlinks 440A-440D support a communication throughput of 4 GB/s, 30 GB/s,80 GB/s or higher, depending on the implementation. Various interconnectprotocols may be used including, but not limited to, PCIe 4.0 or 5.0 andNVLink 2.0. However, the underlying principles of the invention are notlimited to any particular communication protocol or throughput.

In addition, in one embodiment, two or more of the GPUs 410-413 areinterconnected over high-speed links 442A-442B, which may be implementedusing the same or different protocols/links than those used forhigh-speed links 440A-440D. Similarly, two or more of the multi-coreprocessors 405-406 may be connected over high speed link 443 which maybe symmetric multi-processor (SMP) buses operating at 20 GB/s, 30 GB/s,120 GB/s or higher. Alternatively, all communication between the varioussystem components shown in FIG. 4A may be accomplished using the sameprotocols/links (e.g., over a common interconnection fabric). Asmentioned, however, the underlying principles of the invention are notlimited to any particular type of interconnect technology.

In one embodiment, each multi-core processor 405-406 is communicativelycoupled to a processor memory 401-402, via memory interconnects430A-430B, respectively, and each GPU 410-413 is communicatively coupledto GPU memory 420-423 over GPU memory interconnects 450A-450D,respectively. The memory interconnects 430A-430B and 450A-450D mayutilize the same or different memory access technologies. By way ofexample, and not limitation, the processor memories 401-402 and GPUmemories 420-423 may be volatile memories such as dynamic random accessmemories (DRAMs) (including stacked DRAMs), Graphics DDR SDRAM (GDDR)(e.g., GDDR5, GDDR6), or High Bandwidth Memory (HBM) and/or may benon-volatile memories such as 3D XPoint or Nano-Ram. In one embodiment,some portion of the memories may be volatile memory and another portionmay be non-volatile memory (e.g., using a two-level memory (2LM)hierarchy).

As described below, although the various processors 405-406 and GPUs410-413 may be physically coupled to a particular memory 401-402,420-423, respectively, a unified memory architecture may be implementedin which the same virtual system address space (also referred to as the“effective address” space) is distributed among all of the variousphysical memories. For example, processor memories 401-402 may eachcomprise 64 GB of the system memory address space and GPU memories420-423 may each comprise 32 GB of the system memory address space(resulting in a total of 256 GB addressable memory in this example).

FIG. 4B illustrates additional details for an interconnection between amulti-core processor 407 and a graphics acceleration module 446 inaccordance with one embodiment. The graphics acceleration module 446 mayinclude one or more GPU chips integrated on a line card which is coupledto the processor 407 via the high-speed link 440. Alternatively, thegraphics acceleration module 446 may be integrated on the same packageor chip as the processor 407.

The illustrated processor 407 includes a plurality of cores 460A-460D,each with a translation lookaside buffer 461A-461D and one or morecaches 462A-462D. The cores may include various other components forexecuting instructions and processing data which are not illustrated toavoid obscuring the underlying principles of the invention (e.g.,instruction fetch units, branch prediction units, decoders, executionunits, reorder buffers, etc.). The caches 462A-462D may comprise level 1(L1) and level 2 (L2) caches. In addition, one or more shared caches 456may be included in the caching hierarchy and shared by sets of the cores460A-460D. For example, one embodiment of the processor 407 includes 24cores, each with its own L1 cache, twelve shared L2 caches, and twelveshared L3 caches. In this embodiment, one of the L2 and L3 caches areshared by two adjacent cores. The processor 407 and the graphicsaccelerator integration module 446 connect with system memory 441, whichmay include processor memories 401-402.

Coherency is maintained for data and instructions stored in the variouscaches 462A-462D, 456 and system memory 441 via inter-core communicationover a coherence bus 464. For example, each cache may have cachecoherency logic/circuitry associated therewith to communicate to overthe coherence bus 464 in response to detected reads or writes toparticular cache lines. In one implementation, a cache snooping protocolis implemented over the coherence bus 464 to snoop cache accesses. Cachesnooping/coherency techniques are well understood by those of skill inthe art and will not be described in detail here to avoid obscuring theunderlying principles of the invention.

In one embodiment, a proxy circuit 425 communicatively couples thegraphics acceleration module 446 to the coherence bus 464, allowing thegraphics acceleration module 446 to participate in the cache coherenceprotocol as a peer of the cores. In particular, an interface 435provides connectivity to the proxy circuit 425 over high-speed link 440(e.g., a PCIe bus, NVLink, etc.) and an interface 437 connects thegraphics acceleration module 446 to the high-speed link 440.

In one implementation, an accelerator integration circuit 436 providescache management, memory access, context management, and interruptmanagement services on behalf of a plurality of graphics processingengines 431, 432, N of the graphics acceleration module 446. Thegraphics processing engines 431, 432, N may each comprise a separategraphics processing unit (GPU). Alternatively, the graphics processingengines 431, 432, N may comprise different types of graphics processingengines within a GPU such as graphics execution units, media processingengines (e.g., video encoders/decoders), samplers, and blit engines. Inother words, the graphics acceleration module may be a GPU with aplurality of graphics processing engines 431-432, N or the graphicsprocessing engines 431-432, N may be individual GPUs integrated on acommon package, line card, or chip.

In one embodiment, the accelerator integration circuit 436 includes amemory management unit (MMU) 439 for performing various memorymanagement functions such as virtual-to-physical memory translations(also referred to as effective-to-real memory translations) and memoryaccess protocols for accessing system memory 441. The MMU 439 may alsoinclude a translation lookaside buffer (TLB) (not shown) for caching thevirtual/effective to physical/real address translations. In oneimplementation, a cache 438 stores commands and data for efficientaccess by the graphics processing engines 431-432, N. In one embodiment,the data stored in cache 438 and graphics memories 433-434, M is keptcoherent with the core caches 462A-462D, 456 and system memory 411. Asmentioned, this may be accomplished via proxy circuit 425 which takespart in the cache coherency mechanism on behalf of cache 438 andmemories 433-434, M (e.g., sending updates to the cache 438 related tomodifications/accesses of cache lines on processor caches 462A-462D, 456and receiving updates from the cache 438).

A set of registers 445 store context data for threads executed by thegraphics processing engines 431-432, N and a context management circuit448 manages the thread contexts. For example, the context managementcircuit 448 may perform save and restore operations to save and restorecontexts of the various threads during contexts switches (e.g., where afirst thread is saved and a second thread is stored so that the secondthread can be execute by a graphics processing engine). For example, ona context switch, the context management circuit 448 may store currentregister values to a designated region in memory (e.g., identified by acontext pointer). It may then restore the register values when returningto the context. In one embodiment, an interrupt management circuit 447receives and processes interrupts received from system devices.

In one implementation, virtual/effective addresses from a graphicsprocessing engine 431 are translated to real/physical addresses insystem memory 411 by the MMU 439. One embodiment of the acceleratorintegration circuit 436 supports multiple (e.g., 4, 8, 16) graphicsaccelerator modules 446 and/or other accelerator devices. The graphicsaccelerator module 446 may be dedicated to a single application executedon the processor 407 or may be shared between multiple applications. Inone embodiment, a virtualized graphics execution environment ispresented in which the resources of the graphics processing engines431-432, N are shared with multiple applications or virtual machines(VMs). The resources may be subdivided into “slices” which are allocatedto different VMs and/or applications based on the processingrequirements and priorities associated with the VMs and/or applications.

Thus, the accelerator integration circuit acts as a bridge to the systemfor the graphics acceleration module 446 and provides addresstranslation and system memory cache services. In addition, theaccelerator integration circuit 436 may provide virtualizationfacilities for the host processor to manage virtualization of thegraphics processing engines, interrupts, and memory management.

Because hardware resources of the graphics processing engines 431-432, Nare mapped explicitly to the real address space seen by the hostprocessor 407, any host processor can address these resources directlyusing an effective address value. One function of the acceleratorintegration circuit 436, in one embodiment, is the physical separationof the graphics processing engines 431-432, N so that they appear to thesystem as independent units.

As mentioned, in the illustrated embodiment, one or more graphicsmemories 433-434, M are coupled to each of the graphics processingengines 431-432, N, respectively. The graphics memories 433-434, M storeinstructions and data being processed by each of the graphics processingengines 431-432, N. The graphics memories 433-434, M may be volatilememories such as DRAMs (including stacked DRAMs), GDDR memory (e.g.,GDDR5, GDDR6), or HBM, and/or may be non-volatile memories such as 3DXPoint or Nano-Ram.

In one embodiment, to reduce data traffic over the high-speed link 440,biasing techniques are used to ensure that the data stored in graphicsmemories 433-434, M is data which will be used most frequently by thegraphics processing engines 431-432, N and preferably not used by thecores 460A-460D (at least not frequently). Similarly, the biasingmechanism attempts to keep data needed by the cores (and preferably notthe graphics processing engines 431-432, N) within the caches 462A-462D,456 of the cores and system memory 411.

FIG. 4C illustrates another embodiment in which the acceleratorintegration circuit 436 is integrated within the processor 407. In thisembodiment, the graphics processing engines 431-432, N communicatedirectly over the high-speed link 440 to the accelerator integrationcircuit 436 via interface 437 and interface 435 (which, again, may beutilize any form of bus or interface protocol). The acceleratorintegration circuit 436 may perform the same operations as thosedescribed with respect to FIG. 4B, but potentially at a higherthroughput given its close proximity to the coherency bus 464 and caches462A-462D, 456.

One embodiment supports different programming models including adedicated-process programming model (no graphics acceleration modulevirtualization) and shared programming models (with virtualization). Thelatter may include programming models which are controlled by theaccelerator integration circuit 436 and programming models which arecontrolled by the graphics acceleration module 446.

In one embodiment of the dedicated process model, graphics processingengines 431-432, N are dedicated to a single application or processunder a single operating system. The single application can funnel otherapplication requests to the graphics engines 431-432, N, providingvirtualization within a VM/partition.

In the dedicated-process programming models, the graphics processingengines 431-432, N, may be shared by multiple VM/application partitions.The shared models require a system hypervisor to virtualize the graphicsprocessing engines 431-432, N to allow access by each operating system.For single-partition systems without a hypervisor, the graphicsprocessing engines 431-432, N are owned by the operating system. In bothcases, the operating system can virtualize the graphics processingengines 431-432, N to provide access to each process or application.

For the shared programming model, the graphics acceleration module 446or an individual graphics processing engine 431-432, N selects a processelement using a process handle. In one embodiment, process elements arestored in system memory 411 and are addressable using the effectiveaddress to real address translation techniques described herein. Theprocess handle may be an implementation-specific value provided to thehost process when registering its context with the graphics processingengine 431-432, N (that is, calling system software to add the processelement to the process element linked list). The lower 16-bits of theprocess handle may be the offset of the process element within theprocess element linked list.

FIG. 4D illustrates an exemplary accelerator integration slice 490. Asused herein, a “slice” comprises a specified portion of the processingresources of the accelerator integration circuit 436. Applicationeffective address space 482 within system memory 411 stores processelements 483. In one embodiment, the process elements 483 are stored inresponse to GPU invocations 481 from applications 480 executed on theprocessor 407. A process element 483 contains the process state for thecorresponding application 480. A work descriptor (WD) 484 contained inthe process element 483 can be a single job requested by an applicationor may contain a pointer to a queue of jobs. In the latter case, the WD484 is a pointer to the job request queue in the application's addressspace 482.

The graphics acceleration module 446 and/or the individual graphicsprocessing engines 431-432, N can be shared by all or a subset of theprocesses in the system. Embodiments of the invention include aninfrastructure for setting up the process state and sending a WD 484 toa graphics acceleration module 446 to start a job in a virtualizedenvironment.

In one implementation, the dedicated-process programming model isimplementation-specific. In this model, a single process owns thegraphics acceleration module 446 or an individual graphics processingengine 431. Because the graphics acceleration module 446 is owned by asingle process, the hypervisor initializes the accelerator integrationcircuit 436 for the owning partition and the operating systeminitializes the accelerator integration circuit 436 for the owningprocess at the time when the graphics acceleration module 446 isassigned.

In operation, a WD fetch unit 491 in the accelerator integration slice490 fetches the next WD 484 which includes an indication of the work tobe done by one of the graphics processing engines of the graphicsacceleration module 446. Data from the WD 484 may be stored in registers445 and used by the MMU 439, interrupt management circuit 447 and/orcontext management circuit 448 as illustrated. For example, oneembodiment of the MMU 439 includes segment/page walk circuitry foraccessing segment/page tables 486 within the OS virtual address space485. The interrupt management circuit 447 may process interrupt events492 received from the graphics acceleration module 446. When performinggraphics operations, an effective address 493 generated by a graphicsprocessing engine 431-432, N is translated to a real address by the MMU439.

In one embodiment, the same set of registers 445 are duplicated for eachgraphics processing engine 431-432, N and/or graphics accelerationmodule 446 and may be initialized by the hypervisor or operating system.Each of these duplicated registers may be included in an acceleratorintegration slice 490. Exemplary registers that may be initialized bythe hypervisor are shown in Table 1.

TABLE 1 Hypervisor Initialized Registers 1 Slice Control Register 2 RealAddress (RA) Scheduled Processes Area Pointer 3 Authority Mask OverrideRegister 4 Interrupt Vector Table Entry Offset 5 Interrupt Vector TableEntry Limit 6 State Register 7 Logical Partition ID 8 Real address (RA)Hypervisor Accelerator Utilization Record Pointer 9 Storage DescriptionRegister

Exemplary registers that may be initialized by the operating system areshown in Table 2.

TABLE 2 Operating System Initialized Registers 1 Process and ThreadIdentification 2 Effective Address (EA) Context Save/Restore Pointer 3Virtual Address (VA) Accelerator Utilization Record Pointer 4 VirtualAddress (VA) Storage Segment Table Pointer 5 Authority Mask 6 Workdescriptor

In one embodiment, each WD 484 is specific to a particular graphicsacceleration module 446 and/or graphics processing engine 431-432, N. Itcontains all the information a graphics processing engine 431-432, Nrequires to do its work or it can be a pointer to a memory locationwhere the application has set up a command queue of work to becompleted.

FIG. 4E illustrates additional details for one embodiment of a sharedmodel. This embodiment includes a hypervisor real address space 498 inwhich a process element list 499 is stored. The hypervisor real addressspace 498 is accessible via a hypervisor 496 which virtualizes thegraphics acceleration module engines for the operating system 495.

The shared programming models allow for all or a subset of processesfrom all or a subset of partitions in the system to use a graphicsacceleration module 446. There are two programming models where thegraphics acceleration module 446 is shared by multiple processes andpartitions: time-sliced shared and graphics directed shared.

In this model, the system hypervisor 496 owns the graphics accelerationmodule 446 and makes its function available to all operating systems495. For a graphics acceleration module 446 to support virtualization bythe system hypervisor 496, the graphics acceleration module 446 mayadhere to the following requirements: 1) An application's job requestmust be autonomous (that is, the state does not need to be maintainedbetween jobs), or the graphics acceleration module 446 must provide acontext save and restore mechanism. 2) An application's job request isguaranteed by the graphics acceleration module 446 to complete in aspecified amount of time, including any translation faults, or thegraphics acceleration module 446 provides the ability to preempt theprocessing of the job. 3) The graphics acceleration module 446 must beguaranteed fairness between processes when operating in the directedshared programming model.

In one embodiment, for the shared model, the application 480 is requiredto make an operating system 495 system call with a graphics accelerationmodule 446 type, a work descriptor (WD), an authority mask register(AMR) value, and a context save/restore area pointer (CSRP). Thegraphics acceleration module 446 type describes the targetedacceleration function for the system call. The graphics accelerationmodule 446 type may be a system-specific value. The WD is formattedspecifically for the graphics acceleration module 446 and can be in theform of a graphics acceleration module 446 command, an effective addresspointer to a user-defined structure, an effective address pointer to aqueue of commands, or any other data structure to describe the work tobe done by the graphics acceleration module 446. In one embodiment, theAMR value is the AMR state to use for the current process. The valuepassed to the operating system is similar to an application setting theAMR. If the accelerator integration circuit 436 and graphicsacceleration module 446 implementations do not support a User AuthorityMask Override Register (UAMOR), the operating system may apply thecurrent UAMOR value to the AMR value before passing the AMR in thehypervisor call. The hypervisor 496 may optionally apply the currentAuthority Mask Override Register (AMOR) value before placing the AMRinto the process element 483. In one embodiment, the CSRP is one of theregisters 445 containing the effective address of an area in theapplication's address space 482 for the graphics acceleration module 446to save and restore the context state. This pointer is optional if nostate is required to be saved between jobs or when a job is preempted.The context save/restore area may be pinned system memory.

Upon receiving the system call, the operating system 495 may verify thatthe application 480 has registered and been given the authority to usethe graphics acceleration module 446. The operating system 495 thencalls the hypervisor 496 with the information shown in Table 3.

TABLE 3 OS to Hypervisor Call Parameters 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)

Upon receiving the hypervisor call, the hypervisor 496 verifies that theoperating system 495 has registered and been given the authority to usethe graphics acceleration module 446. The hypervisor 496 then puts theprocess element 483 into the process element linked list for thecorresponding graphics acceleration module 446 type. The process elementmay include the information shown in Table 4.

TABLE 4 Process Element Information 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)8 Interrupt vector table, derived from the hypervisor call parameters. 9A state register (SR) value 10 A logical partition ID (LPID) 11 A realaddress (RA) hypervisor accelerator utilization record pointer 12 TheStorage Descriptor Register (SDR)

In one embodiment, the hypervisor initializes a plurality of acceleratorintegration slice 490 registers 445.

As illustrated in FIG. 4F, one embodiment of the invention employs aunified memory addressable via a common virtual memory address spaceused to access the physical processor memories 401-402 and GPU memories420-423. In this implementation, operations executed on the GPUs 410-413utilize the same virtual/effective memory address space to access theprocessors memories 401-402 and vice versa, thereby simplifyingprogrammability. In one embodiment, a first portion of thevirtual/effective address space is allocated to the processor memory401, a second portion to the second processor memory 402, a thirdportion to the GPU memory 420, and so on. The entire virtual/effectivememory space (sometimes referred to as the effective address space) isthereby distributed across each of the processor memories 401-402 andGPU memories 420-423, allowing any processor or GPU to access anyphysical memory with a virtual address mapped to that memory.

In one embodiment, bias/coherence management circuitry 494A-494E withinone or more of the MMUs 439A-439E ensures cache coherence between thecaches of the host processors (e.g., 405) and the GPUs 410-413 andimplements biasing techniques indicating the physical memories in whichcertain types of data should be stored. While multiple instances ofbias/coherence management circuitry 494A-494E are illustrated in FIG.4F, the bias/coherence circuitry may be implemented within the MMU ofone or more host processors 405 and/or within the acceleratorintegration circuit 436.

One embodiment allows GPU-attached memory 420-423 to be mapped as partof system memory, and accessed using shared virtual memory (SVM)technology, but without suffering the typical performance drawbacksassociated with full system cache coherence. The ability to GPU-attachedmemory 420-423 to be accessed as system memory without onerous cachecoherence overhead provides a beneficial operating environment for GPUoffload. This arrangement allows the host processor 405 software tosetup operands and access computation results, without the overhead oftradition I/O DMA data copies. Such traditional copies involve drivercalls, interrupts and memory mapped I/O (MMIO) accesses that are allinefficient relative to simple memory accesses. At the same time, theability to access GPU attached memory 420-423 without cache coherenceoverheads can be critical to the execution time of an offloadedcomputation. In cases with substantial streaming write memory traffic,for example, cache coherence overhead can significantly reduce theeffective write bandwidth seen by a GPU 410-413. The efficiency ofoperand setup, the efficiency of results access, and the efficiency ofGPU computation all play a role in determining the effectiveness of GPUoffload.

In one implementation, the selection of between GPU bias and hostprocessor bias is driven by a bias tracker data structure. A bias tablemay be used, for example, which may be a page-granular structure (i.e.,controlled at the granularity of a memory page) that includes 1 or 2bits per GPU-attached memory page. The bias table may be implemented ina stolen memory range of one or more GPU-attached memories 420-423, withor without a bias cache in the GPU 410-413 (e.g., to cachefrequently/recently used entries of the bias table). Alternatively, theentire bias table may be maintained within the GPU.

In one implementation, the bias table entry associated with each accessto the GPU-attached memory 420-423 is accessed prior the actual accessto the GPU memory, causing the following operations. First, localrequests from the GPU 410-413 that find their page in GPU bias areforwarded directly to a corresponding GPU memory 420-423. Local requestsfrom the GPU that find their page in host bias are forwarded to theprocessor 405 (e.g., over a high-speed link as discussed above). In oneembodiment, requests from the processor 405 that find the requested pagein host processor bias complete the request like a normal memory read.Alternatively, requests directed to a GPU-biased page may be forwardedto the GPU 410-413. The GPU may then transition the page to a hostprocessor bias if it is not currently using the page.

The bias state of a page can be changed either by a software-basedmechanism, a hardware-assisted software-based mechanism, or, for alimited set of cases, a purely hardware-based mechanism.

One mechanism for changing the bias state employs an API call (e.g.OpenCL), which, in turn, calls the GPU's device driver which, in turn,sends a message (or enqueues a command descriptor) to the GPU directingit to change the bias state and, for some transitions, perform a cacheflushing operation in the host. The cache flushing operation is requiredfor a transition from host processor 405 bias to GPU bias, but is notrequired for the opposite transition.

In one embodiment, cache coherency is maintained by temporarilyrendering GPU-biased pages uncacheable by the host processor 405. Toaccess these pages, the processor 405 may request access from the GPU410 which may or may not grant access right away, depending on theimplementation. Thus, to reduce communication between the processor 405and GPU 410 it is beneficial to ensure that GPU-biased pages are thosewhich are required by the GPU but not the host processor 405 and viceversa.

Graphics Processing Pipeline

FIG. 5 illustrates a graphics processing pipeline 500, according to anembodiment. In one embodiment a graphics processor can implement theillustrated graphics processing pipeline 500. The graphics processor canbe included within the parallel processing subsystems as describedherein, such as the parallel processor 200 of FIG. 2A, which, in oneembodiment, is a variant of the parallel processor(s) 112 of FIG. 1. Thevarious parallel processing systems can implement the graphicsprocessing pipeline 500 via one or more instances of the parallelprocessing unit (e.g., parallel processing unit 202 of FIG. 2A) asdescribed herein. For example, a shader unit (e.g., graphicsmultiprocessor 234 of FIG. 2C) may be configured to perform thefunctions of one or more of a vertex processing unit 504, a tessellationcontrol processing unit 508, a tessellation evaluation processing unit512, a geometry processing unit 516, and a fragment/pixel processingunit 524. The functions of data assembler 502, primitive assemblers 506,514, 518, tessellation unit 510, rasterizer 522, and raster operationsunit 526 may also be performed by other processing engines within aprocessing cluster (e.g., processing cluster 214 of FIG. 2A) and acorresponding partition unit (e.g., partition unit 220A-220N of FIG.2A). The graphics processing pipeline 500 may also be implemented usingdedicated processing units for one or more functions. In one embodiment,one or more portions of the graphics processing pipeline 500 can beperformed by parallel processing logic within a general purposeprocessor (e.g., CPU). In one embodiment, one or more portions of thegraphics processing pipeline 500 can access on-chip memory (e.g.,parallel processor memory 222 as in FIG. 2A) via a memory interface 528,which may be an instance of the memory interface 218 of FIG. 2A.

In one embodiment the data assembler 502 is a processing unit thatcollects vertex data for surfaces and primitives. The data assembler 502then outputs the vertex data, including the vertex attributes, to thevertex processing unit 504. The vertex processing unit 504 is aprogrammable execution unit that executes vertex shader programs,lighting and transforming vertex data as specified by the vertex shaderprograms. The vertex processing unit 504 reads data that is stored incache, local or system memory for use in processing the vertex data andmay be programmed to transform the vertex data from an object-basedcoordinate representation to a world space coordinate space or anormalized device coordinate space.

A first instance of a primitive assembler 506 receives vertex attributesfrom the vertex processing unit 504. The primitive assembler 506readings stored vertex attributes as needed and constructs graphicsprimitives for processing by tessellation control processing unit 508.The graphics primitives include triangles, line segments, points,patches, and so forth, as supported by various graphics processingapplication programming interfaces (APIs).

The tessellation control processing unit 508 treats the input verticesas control points for a geometric patch. The control points aretransformed from an input representation from the patch (e.g., thepatch's bases) to a representation that is suitable for use in surfaceevaluation by the tessellation evaluation processing unit 512. Thetessellation control processing unit 508 can also compute tessellationfactors for edges of geometric patches. A tessellation factor applies toa single edge and quantifies a view-dependent level of detail associatedwith the edge. A tessellation unit 510 is configured to receive thetessellation factors for edges of a patch and to tessellate the patchinto multiple geometric primitives such as line, triangle, orquadrilateral primitives, which are transmitted to a tessellationevaluation processing unit 512. The tessellation evaluation processingunit 512 operates on parameterized coordinates of the subdivided patchto generate a surface representation and vertex attributes for eachvertex associated with the geometric primitives.

A second instance of a primitive assembler 514 receives vertexattributes from the tessellation evaluation processing unit 512, readingstored vertex attributes as needed, and constructs graphics primitivesfor processing by the geometry processing unit 516. The geometryprocessing unit 516 is a programmable execution unit that executesgeometry shader programs to transform graphics primitives received fromprimitive assembler 514 as specified by the geometry shader programs. Inone embodiment the geometry processing unit 516 is programmed tosubdivide the graphics primitives into one or more new graphicsprimitives and calculate parameters used to rasterize the new graphicsprimitives.

In some embodiments the geometry processing unit 516 can add or deleteelements in the geometry stream. The geometry processing unit 516outputs the parameters and vertices specifying new graphics primitivesto primitive assembler 518. The primitive assembler 518 receives theparameters and vertices from the geometry processing unit 516 andconstructs graphics primitives for processing by a viewport scale, cull,and clip unit 520. The geometry processing unit 516 reads data that isstored in parallel processor memory or system memory for use inprocessing the geometry data. The viewport scale, cull, and clip unit520 performs clipping, culling, and viewport scaling and outputsprocessed graphics primitives to a rasterizer 522.

The rasterizer 522 can perform depth culling and other depth-basedoptimizations. The rasterizer 522 also performs scan conversion on thenew graphics primitives to generate fragments and output those fragmentsand associated coverage data to the fragment/pixel processing unit 524.The fragment/pixel processing unit 524 is a programmable execution unitthat is configured to execute fragment shader programs or pixel shaderprograms. The fragment/pixel processing unit 524 transforming fragmentsor pixels received from rasterizer 522, as specified by the fragment orpixel shader programs. For example, the fragment/pixel processing unit524 may be programmed to perform operations included but not limited totexture mapping, shading, blending, texture correction and perspectivecorrection to produce shaded fragments or pixels that are output to araster operations unit 526. The fragment/pixel processing unit 524 canread data that is stored in either the parallel processor memory or thesystem memory for use when processing the fragment data. Fragment orpixel shader programs may be configured to shade at sample, pixel, tile,or other granularities depending on the sampling rate configured for theprocessing units.

The raster operations unit 526 is a processing unit that performs rasteroperations including, but not limited to stencil, z test, blending, andthe like, and outputs pixel data as processed graphics data to be storedin graphics memory (e.g., parallel processor memory 222 as in FIG. 2A,and/or system memory 104 as in FIG. 1), to be displayed on the one ormore display device(s) 110 or for further processing by one of the oneor more processor(s) 102 or parallel processor(s) 112. In someembodiments the raster operations unit 526 is configured to compress zor color data that is written to memory and decompress z or color datathat is read from memory.

Machine Learning Overview

A machine learning algorithm is an algorithm that can learn based on aset of data. Embodiments of machine learning algorithms can be designedto model high-level abstractions within a data set. For example, imagerecognition algorithms can be used to determine which of severalcategories to which a given input belong; regression algorithms canoutput a numerical value given an input; and pattern recognitionalgorithms can be used to generate translated text or perform text tospeech and/or speech recognition.

An exemplary type of machine learning algorithm is a neural network.There are many types of neural networks; a simple type of neural networkis a feedforward network. A feedforward network may be implemented as anacyclic graph in which the nodes are arranged in layers. Typically, afeedforward network topology includes an input layer and an output layerthat are separated by at least one hidden layer. The hidden layertransforms input received by the input layer into a representation thatis useful for generating output in the output layer. The network nodesare fully connected via edges to the nodes in adjacent layers, but thereare no edges between nodes within each layer. Data received at the nodesof an input layer of a feedforward network are propagated (i.e., “fedforward”) to the nodes of the output layer via an activation functionthat calculates the states of the nodes of each successive layer in thenetwork based on coefficients (“weights”) respectively associated witheach of the edges connecting the layers. Depending on the specific modelbeing represented by the algorithm being executed, the output from theneural network algorithm can take various forms.

Before a machine learning algorithm can be used to model a particularproblem, the algorithm is trained using a training data set. Training aneural network involves selecting a network topology, using a set oftraining data representing a problem being modeled by the network, andadjusting the weights until the network model performs with a minimalerror for all instances of the training data set. For example, during asupervised learning training process for a neural network, the outputproduced by the network in response to the input representing aninstance in a training data set is compared to the “correct” labeledoutput for that instance, an error signal representing the differencebetween the output and the labeled output is calculated, and the weightsassociated with the connections are adjusted to minimize that error asthe error signal is backward propagated through the layers of thenetwork. The network is considered “trained” when the errors for each ofthe outputs generated from the instances of the training data set areminimized.

The accuracy of a machine learning algorithm can be affectedsignificantly by the quality of the data set used to train thealgorithm. The training process can be computationally intensive and mayrequire a significant amount of time on a conventional general-purposeprocessor. Accordingly, parallel processing hardware is used to trainmany types of machine learning algorithms. This is particularly usefulfor optimizing the training of neural networks, as the computationsperformed in adjusting the coefficients in neural networks lendthemselves naturally to parallel implementations. Specifically, manymachine learning algorithms and software applications have been adaptedto make use of the parallel processing hardware within general-purposegraphics processing devices.

FIG. 6 is a generalized diagram of a machine learning software stack600. A machine learning application 602 can be configured to train aneural network using a training dataset or to use a trained deep neuralnetwork to implement machine intelligence. The machine learningapplication 602 can include training and inference functionality for aneural network and/or specialized software that can be used to train aneural network before deployment. The machine learning application 602can implement any type of machine intelligence including but not limitedto image recognition, mapping and localization, autonomous navigation,speech synthesis, medical imaging, or language translation.

Hardware acceleration for the machine learning application 602 can beenabled via a machine learning framework 604. The machine learningframework 604 can provide a library of machine learning primitives.Machine learning primitives are basic operations that are commonlyperformed by machine learning algorithms. Without the machine learningframework 604, developers of machine learning algorithms would berequired to create and optimize the main computational logic associatedwith the machine learning algorithm, then re-optimize the computationallogic as new parallel processors are developed. Instead, the machinelearning application can be configured to perform the necessarycomputations using the primitives provided by the machine learningframework 604. Exemplary primitives include tensor convolutions,activation functions, and pooling, which are computational operationsthat are performed while training a convolutional neural network (CNN).The machine learning framework 604 can also provide primitives toimplement basic linear algebra subprograms performed by manymachine-learning algorithms, such as matrix and vector operations.

The machine learning framework 604 can process input data received fromthe machine learning application 602 and generate the appropriate inputto a compute framework 606. The compute framework 606 can abstract theunderlying instructions provided to the GPGPU driver 608 to enable themachine learning framework 604 to take advantage of hardwareacceleration via the GPGPU hardware 610 without requiring the machinelearning framework 604 to have intimate knowledge of the architecture ofthe GPGPU hardware 610. Additionally, the compute framework 606 canenable hardware acceleration for the machine learning framework 604across a variety of types and generations of the GPGPU hardware 610.

GPGPU Machine Learning Acceleration

FIG. 7 illustrates a highly-parallel general-purpose graphics processingunit 700, according to an embodiment. In one embodiment thegeneral-purpose processing unit (GPGPU) 700 can be configured to beparticularly efficient in processing the type of computational workloadsassociated with training deep neural networks. Additionally, the GPGPU700 can be linked directly to other instances of the GPGPU to create amulti-GPU cluster to improve training speed for particularly deep neuralnetworks.

The GPGPU 700 includes a host interface 702 to enable a connection witha host processor. In one embodiment the host interface 702 is a PCIExpress interface. However, the host interface can also be a vendorspecific communications interface or communications fabric. The GPGPU700 receives commands from the host processor and uses a globalscheduler 704 to distribute execution threads associated with thosecommands to a set of compute clusters 706A-706H. The compute clusters706A-706H share a cache memory 708. The cache memory 708 can serve as ahigher-level cache for cache memories within the compute clusters706A-706H.

The GPGPU 700 includes memory 714A-714B coupled with the computeclusters 706A-H via a set of memory controllers 712A-712B. In variousembodiments, the memory 714A-714B can include various types of memorydevices including dynamic random access memory (DRAM) or graphics randomaccess memory, such as synchronous graphics random access memory(SGRAM), including graphics double data rate (GDDR) memory, or 3Dstacked memory, including but not limited to high bandwidth memory(HBM).

In one embodiment each compute cluster 706A-706H includes a set ofgraphics multiprocessors, such as the graphics multiprocessor 400 ofFIG. 4A. The graphics multiprocessors of the compute cluster multipletypes of integer and floating point logic units that can performcomputational operations at a range of precisions including suited formachine learning computations. For example and in one embodiment atleast a subset of the floating point units in each of the computeclusters 706A-706H can be configured to perform 16-bit or 32-bitfloating point operations, while a different subset of the floatingpoint units can be configured to perform 64-bit floating pointoperations.

Multiple instances of the GPGPU 700 can be configured to operate as acompute cluster. The communication mechanism used by the compute clusterfor synchronization and data exchange varies across embodiments. In oneembodiment the multiple instances of the GPGPU 700 communicate over thehost interface 702. In one embodiment the GPGPU 700 includes an I/O hub709 that couples the GPGPU 700 with a GPU link 710 that enables a directconnection to other instances of the GPGPU. In one embodiment the GPUlink 710 is coupled to a dedicated GPU-to-GPU bridge that enablescommunication and synchronization between multiple instances of theGPGPU 700. In one embodiment the GPU link 710 couples with a high speedinterconnect to transmit and receive data to other GPGPUs or parallelprocessors. In one embodiment the multiple instances of the GPGPU 700are located in separate data processing systems and communicate via anetwork device that is accessible via the host interface 702. In oneembodiment the GPU link 710 can be configured to enable a connection toa host processor in addition to or as an alternative to the hostinterface 702.

While the illustrated configuration of the GPGPU 700 can be configuredto train neural networks, one embodiment provides alternateconfiguration of the GPGPU 700 that can be configured for deploymentwithin a high performance or low power inferencing platform. In aninferencing configuration the GPGPU 700 includes fewer of the computeclusters 706A-706H relative to the training configuration. Additionallymemory technology associated with the memory 714A-714B may differbetween inferencing and training configurations. In one embodiment theinferencing configuration of the GPGPU 700 can support inferencingspecific instructions. For example, an inferencing configuration canprovide support for one or more 8-bit integer dot product instructions,which are commonly used during inferencing operations for deployedneural networks.

FIG. 8 illustrates a multi-GPU computing system 800, according to anembodiment. The multi-GPU computing system 800 can include a processor802 coupled to multiple GPGPUs 806A-806D via a host interface switch804. The host interface switch 804, in one embodiment, is a PCI expressswitch device that couples the processor 802 to a PCI express bus overwhich the processor 802 can communicate with the set of GPGPUs806A-806D. Each of the multiple GPGPUs 806A-806D can be an instance ofthe GPGPU 700 of FIG. 7. The GPGPUs 806A-806D can interconnect via a setof high-speed point-to-point GPU to GPU links 816. The high-speed GPU toGPU links can connect to each of the GPGPUs 806A-806D via a dedicatedGPU link, such as the GPU link 710 as in FIG. 7. The P2P GPU links 816enable direct communication between each of the GPGPUs 806A-806D withoutrequiring communication over the host interface bus to which theprocessor 802 is connected. With GPU-to-GPU traffic directed to the P2PGPU links, the host interface bus remains available for system memoryaccess or to communicate with other instances of the multi-GPU computingsystem 800, for example, via one or more network devices. While in theillustrated embodiment the GPGPUs 806A-806D connect to the processor 802via the host interface switch 804, in one embodiment the processor 802includes direct support for the P2P GPU links 816 and can connectdirectly to the GPGPUs 806A-806D.

Machine Learning Neural Network Implementations

The computing architecture provided by embodiments described herein canbe configured to perform the types of parallel processing that isparticularly suited for training and deploying neural networks formachine learning. A neural network can be generalized as a network offunctions having a graph relationship. As is well-known in the art,there are a variety of types of neural network implementations used inmachine learning. One exemplary type of neural network is thefeedforward network, as previously described.

A second exemplary type of neural network is the Convolutional NeuralNetwork (CNN). A CNN is a specialized feedforward neural network forprocessing data having a known, grid-like topology, such as image data.Accordingly, CNNs are commonly used for compute vision and imagerecognition applications, but they also may be used for other types ofpattern recognition such as speech and language processing. The nodes inthe CNN input layer are organized into a set of “filters” (featuredetectors inspired by the receptive fields found in the retina), and theoutput of each set of filters is propagated to nodes in successivelayers of the network. The computations for a CNN include applying theconvolution mathematical operation to each filter to produce the outputof that filter. Convolution is a specialized kind of mathematicaloperation performed by two functions to produce a third function that isa modified version of one of the two original functions. Inconvolutional network terminology, the first function to the convolutioncan be referred to as the input, while the second function can bereferred to as the convolution kernel. The output may be referred to asthe feature map. For example, the input to a convolution layer can be amultidimensional array of data that defines the various color componentsof an input image. The convolution kernel can be a multidimensionalarray of parameters, where the parameters are adapted by the trainingprocess for the neural network.

Recurrent neural networks (RNNs) are a family of feedforward neuralnetworks that include feedback connections between layers. RNNs enablemodeling of sequential data by sharing parameter data across differentparts of the neural network. The architecture for a RNN includes cycles.The cycles represent the influence of a present value of a variable onits own value at a future time, as at least a portion of the output datafrom the RNN is used as feedback for processing subsequent input in asequence. This feature makes RNNs particularly useful for languageprocessing due to the variable nature in which language data can becomposed.

The figures described below present exemplary feedforward, CNN, and RNNnetworks, as well as describe a general process for respectivelytraining and deploying each of those types of networks. It will beunderstood that these descriptions are exemplary and non-limiting as toany specific embodiment described herein and the concepts illustratedcan be applied generally to deep neural networks and machine learningtechniques in general.

The exemplary neural networks described above can be used to performdeep learning. Deep learning is machine learning using deep neuralnetworks. The deep neural networks used in deep learning are artificialneural networks composed of multiple hidden layers, as opposed toshallow neural networks that include only a single hidden layer. Deeperneural networks are generally more computationally intensive to train.However, the additional hidden layers of the network enable multisteppattern recognition that results in reduced output error relative toshallow machine learning techniques.

Deep neural networks used in deep learning typically include a front-endnetwork to perform feature recognition coupled to a back-end networkwhich represents a mathematical model that can perform operations (e.g.,object classification, speech recognition, etc.) based on the featurerepresentation provided to the model. Deep learning enables machinelearning to be performed without requiring hand crafted featureengineering to be performed for the model. Instead, deep neural networkscan learn features based on statistical structure or correlation withinthe input data. The learned features can be provided to a mathematicalmodel that can map detected features to an output. The mathematicalmodel used by the network is generally specialized for the specific taskto be performed, and different models will be used to perform differenttask.

Once the neural network is structured, a learning model can be appliedto the network to train the network to perform specific tasks. Thelearning model describes how to adjust the weights within the model toreduce the output error of the network. Backpropagation of errors is acommon method used to train neural networks. An input vector ispresented to the network for processing. The output of the network iscompared to the desired output using a loss function and an error valueis calculated for each of the neurons in the output layer. The errorvalues are then propagated backwards until each neuron has an associatederror value which roughly represents its contribution to the originaloutput. The network can then learn from those errors using an algorithm,such as the stochastic gradient descent algorithm, to update the weightsof the of the neural network.

FIG. 9A-9B illustrate an exemplary convolutional neural network. FIG. 9Aillustrates various layers within a CNN. As shown in FIG. 9A, anexemplary CNN used to model image processing can receive input 902describing the red, green, and blue (RGB) components of an input image.The input 902 can be processed by multiple convolutional layers (e.g.,convolutional layer 904, convolutional layer 906). The output from themultiple convolutional layers may optionally be processed by a set offully connected layers 908. Neurons in a fully connected layer have fullconnections to all activations in the previous layer, as previouslydescribed for a feedforward network. The output from the fully connectedlayers 908 can be used to generate an output result from the network.The activations within the fully connected layers 908 can be computedusing matrix multiplication instead of convolution. Not all CNNimplementations are configured to make use of fully connected layers908. For example, in some implementations the convolutional layer 906can generate output for the CNN.

The convolutional layers are sparsely connected, which differs fromtraditional neural network configuration found in the fully connectedlayers 908. Traditional neural network layers are fully connected, suchthat every output unit interacts with every input unit. However, theconvolutional layers are sparsely connected because the output of theconvolution of a field is input (instead of the respective state valueof each of the nodes in the field) to the nodes of the subsequent layer,as illustrated. The kernels associated with the convolutional layersperform convolution operations, the output of which is sent to the nextlayer. The dimensionality reduction performed within the convolutionallayers is one aspect that enables the CNN to scale to process largeimages.

FIG. 9B illustrates exemplary computation stages within a convolutionallayer of a CNN. Input to a convolutional layer 912 of a CNN can beprocessed in three stages of a convolutional layer 914. The three stagescan include a convolution stage 916, a detector stage 918, and a poolingstage 920. The convolutional layer 914 can then output data to asuccessive convolutional layer. The final convolutional layer of thenetwork can generate output feature map data or provide input to a fullyconnected layer, for example, to generate a classification value for theinput to the CNN.

In the convolution stage 916 performs several convolutions in parallelto produce a set of linear activations. The convolution stage 916 caninclude an affine transformation, which is any transformation that canbe specified as a linear transformation plus a translation. Affinetransformations include rotations, translations, scaling, andcombinations of these transformations. The convolution stage computesthe output of functions (e.g., neurons) that are connected to specificregions in the input, which can be determined as the local regionassociated with the neuron. The neurons compute a dot product betweenthe weights of the neurons and the region in the local input to whichthe neurons are connected. The output from the convolution stage 916defines a set of linear activations that are processed by successivestages of the convolutional layer 914.

The linear activations can be processed by a detector stage 918. In thedetector stage 918, each linear activation is processed by a non-linearactivation function. The non-linear activation function increases thenonlinear properties of the overall network without affecting thereceptive fields of the convolution layer. Several types of non-linearactivation functions may be used. One particular type is the rectifiedlinear unit (ReLU), which uses an activation function defined asƒ(x)=max (0,x), such that the activation is thresholded at zero.

The pooling stage 920 uses a pooling function that replaces the outputof the convolutional layer 906 with a summary statistic of the nearbyoutputs. The pooling function can be used to introduce translationinvariance into the neural network, such that small translations to theinput do not change the pooled outputs. Invariance to local translationcan be useful in scenarios where the presence of a feature in the inputdata is more important than the precise location of the feature. Varioustypes of pooling functions can be used during the pooling stage 920,including max pooling, average pooling, and l2-norm pooling.Additionally, some CNN implementations do not include a pooling stage.Instead, such implementations substitute and additional convolutionstage having an increased stride relative to previous convolutionstages.

The output from the convolutional layer 914 can then be processed by thenext layer 922. The next layer 922 can be an additional convolutionallayer or one of the fully connected layers 908. For example, the firstconvolutional layer 904 of FIG. 9A can output to the secondconvolutional layer 906, while the second convolutional layer can outputto a first layer of the fully connected layers 908.

FIG. 10 illustrates an exemplary recurrent neural network 1000. In arecurrent neural network (RNN), the previous state of the networkinfluences the output of the current state of the network. RNNs can bebuilt in a variety of ways using a variety of functions. The use of RNNsgenerally revolves around using mathematical models to predict thefuture based on a prior sequence of inputs. For example, an RNN may beused to perform statistical language modeling to predict an upcomingword given a previous sequence of words. The illustrated RNN 1000 can bedescribed has having an input layer 1002 that receives an input vector,hidden layers 1004 to implement a recurrent function, a feedbackmechanism 1005 to enable a ‘memory’ of previous states, and an outputlayer 1006 to output a result. The RNN 1000 operates based ontime-steps. The state of the RNN at a given time step is influencedbased on the previous time step via the feedback mechanism 1005. For agiven time step, the state of the hidden layers 1004 is defined by theprevious state and the input at the current time step. An initial input(x₁) at a first time step can be processed by the hidden layer 1004. Asecond input (x₂) can be processed by the hidden layer 1004 using stateinformation that is determined during the processing of the initialinput (x₁). A given state can be computed as s_(t)=ƒ(Ux_(t)+Ws_(t−1)),where U and W are parameter matrices. The function ƒ is generally anonlinearity, such as the hyperbolic tangent function (Tanh) or avariant of the rectifier function ƒ(x)=max(0,x). However, the specificmathematical function used in the hidden layers 1004 can vary dependingon the specific implementation details of the RNN 1000.

In addition to the basic CNN and RNN networks described, variations onthose networks may be enabled. One example RNN variant is the long shortterm memory (LSTM) RNN. LSTM RNNs are capable of learning long-termdependencies that may be necessary for processing longer sequences oflanguage. A variant on the CNN is a convolutional deep belief network,which has a structure similar to a CNN and is trained in a mannersimilar to a deep belief network. A deep belief network (DBN) is agenerative neural network that is composed of multiple layers ofstochastic (random) variables. DBNs can be trained layer-by-layer usinggreedy unsupervised learning. The learned weights of the DBN can then beused to provide pre-train neural networks by determining an optimalinitial set of weights for the neural network.

FIG. 11 illustrates training and deployment of a deep neural network.Once a given network has been structured for a task the neural networkis trained using a training dataset 1102. Various training frameworks1104 have been developed to enable hardware acceleration of the trainingprocess. For example, the machine learning framework 604 of FIG. 6 maybe configured as a training framework 1104. The training framework 1104can hook into an untrained neural network 1106 and enable the untrainedneural net to be trained using the parallel processing resourcesdescribed herein to generate a trained neural network 1108.

To start the training process the initial weights may be chosen randomlyor by pre-training using a deep belief network. The training cycle thenbe performed in either a supervised or unsupervised manner.

Supervised learning is a learning method in which training is performedas a mediated operation, such as when the training dataset 1102 includesinput paired with the desired output for the input, or where thetraining dataset includes input having known output and the output ofthe neural network is manually graded. The network processes the inputsand compares the resulting outputs against a set of expected or desiredoutputs. Errors are then propagated back through the system. Thetraining framework 1104 can adjust to adjust the weights that controlthe untrained neural network 1106. The training framework 1104 canprovide tools to monitor how well the untrained neural network 1106 isconverging towards a model suitable to generating correct answers basedon known input data. The training process occurs repeatedly as theweights of the network are adjusted to refine the output generated bythe neural network. The training process can continue until the neuralnetwork reaches a statistically desired accuracy associated with atrained neural network 1108. The trained neural network 1108 can then bedeployed to implement any number of machine learning operations togenerate a result 1114 based on the input of new data 1112.

Unsupervised learning is a learning method in which the network attemptsto train itself using unlabeled data. Thus, for unsupervised learningthe training dataset 1102 will include input data without any associatedoutput data. The untrained neural network 1106 can learn groupingswithin the unlabeled input and can determine how individual inputs arerelated to the overall dataset. Unsupervised training can be used togenerate a self-organizing map, which is a type of trained neuralnetwork 1107 capable of performing operations useful in reducing thedimensionality of data. Unsupervised training can also be used toperform anomaly detection, which allows the identification of datapoints in an input dataset that deviate from the normal patterns of thedata.

Variations on supervised and unsupervised training may also be employed.Semi-supervised learning is a technique in which in the training dataset1102 includes a mix of labeled and unlabeled data of the samedistribution. Incremental learning is a variant of supervised learningin which input data is continuously used to further train the model.Incremental learning enables the trained neural network 1108 to adapt tothe new data 1112 without forgetting the knowledge instilled within thenetwork during initial training.

Whether supervised or unsupervised, the training process forparticularly deep neural networks may be too computationally intensivefor a single compute node. Instead of using a single compute node, adistributed network of computational nodes can be used to accelerate thetraining process.

FIG. 12 is a block diagram illustrating distributed learning.Distributed learning is a training model that uses multiple distributedcomputing nodes to perform supervised or unsupervised training of aneural network. The distributed computational nodes can each include oneor more host processors and one or more of the general-purposeprocessing nodes, such as the highly-parallel general-purpose graphicsprocessing unit 700 as in FIG. 7. As illustrated, distributed learningcan be performed model parallelism 1202, data parallelism 1204, or acombination of model and data parallelism 1204.

In model parallelism 1202, different computational nodes in adistributed system can perform training computations for different partsof a single network. For example, each layer of a neural network can betrained by a different processing node of the distributed system. Thebenefits of model parallelism include the ability to scale toparticularly large models. Splitting the computations associated withdifferent layers of the neural network enables the training of verylarge neural networks in which the weights of all layers would not fitinto the memory of a single computational node. In some instances, modelparallelism can be particularly useful in performing unsupervisedtraining of large neural networks.

In data parallelism 1204, the different nodes of the distributed networkhave a complete instance of the model and each node receives a differentportion of the data. The results from the different nodes are thencombined. While different approaches to data parallelism are possible,data parallel training approaches all require a technique of combiningresults and synchronizing the model parameters between each node.Exemplary approaches to combining data include parameter averaging andupdate based data parallelism. Parameter averaging trains each node on asubset of the training data and sets the global parameters (e.g.,weights, biases) to the average of the parameters from each node.Parameter averaging uses a central parameter server that maintains theparameter data. Update based data parallelism is similar to parameteraveraging except that instead of transferring parameters from the nodesto the parameter server, the updates to the model are transferred.Additionally, update based data parallelism can be performed in adecentralized manner, where the updates are compressed and transferredbetween nodes.

Combined model and data parallelism 1206 can be implemented, forexample, in a distributed system in which each computational nodeincludes multiple GPUs. Each node can have a complete instance of themodel with separate GPUs within each node are used to train differentportions of the model.

Distributed training has increased overhead relative to training on asingle machine. However, the parallel processors and GPGPUs describedherein can each implement various techniques to reduce the overhead ofdistributed training, including techniques to enable high bandwidthGPU-to-GPU data transfer and accelerated remote data synchronization.

Exemplary Machine Learning Applications

Machine learning can be applied to solve a variety of technologicalproblems, including but not limited to computer vision, autonomousdriving and navigation, speech recognition, and language processing.Computer vision has traditionally been one of the most active researchareas for machine learning applications. Applications of computer visionrange from reproducing human visual abilities, such as recognizingfaces, to creating new categories of visual abilities. For example,computer vision applications can be configured to recognize sound wavesfrom the vibrations induced in objects visible in a video. Parallelprocessor accelerated machine learning enables computer visionapplications to be trained using significantly larger training datasetthan previously feasible and enables inferencing systems to be deployedusing low power parallel processors.

Parallel processor accelerated machine learning has autonomous drivingapplications including lane and road sign recognition, obstacleavoidance, navigation, and driving control. Accelerated machine learningtechniques can be used to train driving models based on datasets thatdefine the appropriate responses to specific training input. Theparallel processors described herein can enable rapid training of theincreasingly complex neural networks used for autonomous drivingsolutions and enables the deployment of low power inferencing processorsin a mobile platform suitable for integration into autonomous vehicles.

Parallel processor accelerated deep neural networks have enabled machinelearning approaches to automatic speech recognition (ASR). ASR includesthe creation of a function that computes the most probable linguisticsequence given an input acoustic sequence. Accelerated machine learningusing deep neural networks have enabled the replacement of the hiddenMarkov models (HMMs) and Gaussian mixture models (GMMs) previously usedfor ASR.

Parallel processor accelerated machine learning can also be used toaccelerate natural language processing. Automatic learning procedurescan make use of statistical inference algorithms to produce models thatare robust to erroneous or unfamiliar input. Exemplary natural languageprocessor applications include automatic machine translation betweenhuman languages.

The parallel processing platforms used for machine learning can bedivided into training platforms and deployment platforms. Trainingplatforms are generally highly parallel and include optimizations toaccelerate multi-GPU single node training and multi-node, multi-GPUtraining. Exemplary parallel processors suited for training include thehighly-parallel general-purpose graphics processing unit 700 of FIG. 7and the multi-GPU computing system 800 of FIG. 8. On the contrary,deployed machine learning platforms generally include lower powerparallel processors suitable for use in products such as cameras,autonomous robots, and autonomous vehicles.

FIG. 13 illustrates an exemplary inferencing system on a chip (SOC) 1300suitable for performing inferencing using a trained model. The SOC 1300can integrate processing components including a media processor 1302, avision processor 1304, a GPGPU 1306 and a multi-core processor 1308. TheSOC 1300 can additionally include on-chip memory 1305 that can enable ashared on-chip data pool that is accessible by each of the processingcomponents. The processing components can be optimized for low poweroperation to enable deployment to a variety of machine learningplatforms, including autonomous vehicles and autonomous robots. Forexample, one implementation of the SOC 1300 can be used as a portion ofthe main control system for an autonomous vehicle. Where the SOC 1300 isconfigured for use in autonomous vehicles the SOC is designed andconfigured for compliance with the relevant functional safety standardsof the deployment jurisdiction.

During operation, the media processor 1302 and vision processor 1304 canwork in concert to accelerate computer vision operations. The mediaprocessor 1302 can enable low latency decode of multiple high-resolution(e.g., 4K, 8K) video streams. The decoded video streams can be writtento a buffer in the on-chip memory 1305. The vision processor 1304 canthen parse the decoded video and perform preliminary processingoperations on the frames of the decoded video in preparation ofprocessing the frames using a trained image recognition model. Forexample, the vision processor 1304 can accelerate convolution operationsfor a CNN that is used to perform image recognition on thehigh-resolution video data, while back end model computations areperformed by the GPGPU 1306.

The multi-core processor 1308 can include control logic to assist withsequencing and synchronization of data transfers and shared memoryoperations performed by the media processor 1302 and the visionprocessor 1304. The multi-core processor 1308 can also function as anapplication processor to execute software applications that can make useof the inferencing compute capability of the GPGPU 1306. For example, atleast a portion of the navigation and driving logic can be implementedin software executing on the multi-core processor 1308. Such softwarecan directly issue computational workloads to the GPGPU 1306 or thecomputational workloads can be issued to the multi-core processor 1308,which can offload at least a portion of those operations to the GPGPU1306.

The GPGPU 1306 can include compute clusters such as a low powerconfiguration of the compute clusters 706A-706H within thehighly-parallel general-purpose graphics processing unit 700. Thecompute clusters within the GPGPU 1306 can support instruction that arespecifically optimized to perform inferencing computations on a trainedneural network. For example, the GPGPU 1306 can support instructions toperform low precision computations such as 8-bit and 4-bit integervector operations.

Specialized Hardware for Efficient Machine Learning Operations

Embodiments described herein provide high-level machine learningcomputational primitives that can be used to abstract many of theunderlying computational details of performing machine learningcalculations. The high-level primitives described herein enable softwarelogic to request high-level machine learning operations whileabstracting the underlying implementation details of those operations.For example and in one embodiment, software logic can request aconvolution operation for an image using a given set of filters. Asingle high-level instruction can be executed that has operands todefine input and output buffer addresses and addresses for buffersstoring filter and/or kernel data. The GPGPU can then divide thehigh-level convolution instruction into multiple sub-operations that areperformed by the underlying compute units of the GPGPU. In oneembodiment direct hardware support for one or more subroutines of thebasic linear algebra subprograms (BLAS) is provided, althoughembodiments can provide hardware support for other libraries ofsubroutines. Compiler logic and associated runtime libraries can compilesource code that make use of supported high-level compute subroutinesand output compiled source code that calls into a machine learningmacro-instruction unit.

Instructions and Logic to Perform Compute Operations for MachineLearning

Hardware accelerators for computer vision and machine learning canimprove energy efficiency for applications such as object, face andspeech recognition by orders of magnitude. These accelerators useinterconnected processing element (PE) arrays, with multiply-addcircuits being performance, area, and energy dominant for mapping keyalgorithms used for CNN compute operations. For example, some machinelearning hardware accelerators use narrow bit-width (16b) fixed-pointmultiply-add datapath building blocks to meet the stringent memory, areaand power budgets for SoCs in the low-power or embedded space. Betterquality of results can be achieved for some datasets and algorithms withthe higher dynamic range offered by floating-point numbers/computationswhile still maintaining the same memory footprint (16 b operands). Priorhardware solutions to accommodate both types of numeric computationsemploy separate fixed-point and floating-point datapath or PEs,resulting in a high area cost to achieve this flexibility. Instead,embodiment described herein provide a merged integer/floating-pointfused multiply-add and multiply-accumulate datapath that utilizesexisting signed integer multiply-add circuits to accomplishfloating-point mantissa multiply-add operations. In one embodiment, byadding only the circuits required for alignment/normalization shifts andexponent unit, floating-point support is enabled in a combinefloating-point/integer unit without increasing input/output data widthand data memory footprint. A single control signal is utilized toswitch, on a per-cycle basis, between floating-point and integer computemodes.

The combined integer/floating-point units provided by embodiments aresupplemented multiple types of machine learning acceleration units thatcan be integrated into a GPGPU. Embodiments described herein providelogic to enable additional instructions that combines afused-multiply-add operation with a neural network activation function,such as the rectified linear unit function (RELU), the sigmoid function,or the hard-sigmoid function.

One embodiment enables an extension of the 16-bit floating-pointencoding to support alternate encodings from the standard IEEE 754half-precision floating-point format. The IEEE half-precisionfloating-point format specifies a 1-bit sign, a 5-bit exponent and a10-bit fractional portion. Embodiments described herein can selectivelysupport alternative encodings of FP16 data based on the pattern of datato be encoded. In one embodiment a supported alternative formatspecifies a 1-bit sign, with an 8-bit exponent and a 7-bit fractionalcomponent. One embodiment allows encoding with a 1-bit sign, a 3-bitexponent, and a 12-bit fractional component. In such embodiments,differing sets of instructions support different floating pointencodings, allowing a developer to select an encoding based on theinstruction specified in program code. In one embodiment, differingfloating point encodings can be used when rounding or down samplingfloating-point data, for example, from an accumulated 32-bitfloating-point value to a 16-bit value.

The merged floating point units described herein can selectively perform16-bit integer or floating point operations on a per-cycle basis. Oneembodiment enables dynamic reconfiguration of the floating-point unitsdescribed herein to enable multi-format support. For example, using amulti-pass configuration, 16-bit integer or floating point units can beconfigured to perform a two-pass 32-bit operation or a four-pass 64-bitoperation. Such logic enables floating point logic that is optimized forlower-precision inferencing operations to be clustered for use inhigher-precision training operations.

One embodiment provides a stochastic rounding unit and statisticsaccumulator for low-precision networks. The stochastic rounding enablesincreased accuracy over classical quantization and rounding forlow-precision deep neural networks. The rounding unit can work indifferent modes. A first mode is a random mode that uses a random numbergenerator to control the rounding unit. A second mode uses a probabilitydistribution of outputs over subsequent inputs and make use of anear-data statistics estimator unit coupled to GPGPU memory.

The techniques described herein can be implemented within ageneral-purpose computational system with machine learning optimizationsprovided via machine-learning accelerator units. A multiprocessorprovided by embodiments described herein is shown in FIG. 14.

FIG. 14 is a block diagram of a multiprocessor unit 1400, according toan embodiment. The multiprocessor unit 1400 can be a variant of agraphics multiprocessor 234 of FIG. 2D. The multiprocessor unit 1400includes a fetch and decode unit 1402, a branch unit 1404, a registerfile 1406, a thread manager 1407, a single instruction multiple threadunit (SIMT unit 1410), and a voltage and frequency manager 1420. Thefetch and decode unit 1402 can fetch an instruction for execution by themultiprocessor unit 1400. The branch unit 1404 can compute instructionpointer adjustments based on an executed jump instruction. The registerfile 1406 can store general-purpose and architectural registers used bythe SIMT unit 1410. The thread manager 1407 can distribute andre-distribute threads among the compute units of the SIMT unit 1410. Inone embodiment, the SIMT unit 1410 is configured to execute a singleinstruction as multiple threads, with each thread of the instructionexecuted by a separate compute unit. In one embodiment compute unit 1411through compute unit 1418 each includes an integer ALU (e.g., ALU1411A-1418A) and a floating-point unit (e.g., FPU 1411B-1418B). Thevoltage and frequency of each compute unit 1411-1418 within the SIMTunit 1410 can be dynamically managed by the voltage and frequencymanager 1420, which can increase or decrease the voltage and clockfrequency supplied to the various compute units as components of thecompute units are enabled and disabled.

In some previously enable configurations, each compute unit can executea single thread of either an integer instruction or a floating-pointinstruction. If any of ALU 1411A-1418A is tasked to execute a thread ofan integer instruction, respective FPU 1411B-FPU 1418B is unavailablefor use to execute a thread of a floating-point instruction and may bepower gated during the operation of the corresponding ALU 1411A-ALU1418A. For example, while ALU 1411A may execute a thread of an integerinstruction while FPU 1413B executes a thread of a floating-pointinstruction, FPU 1411B is power gated while ALU 1411A is active.Embodiments described herein overcome such limitations by enabling, forexample, ALU 1411A to execute thread of an instruction while FPU 1411Bexecutes a thread of a different instruction. Furthermore, oneembodiment provides support for mixed precision or mixed data-typeoperands, such that a single compute unit can simultaneously performoperations for an instruction having floating-point and integer operandsand/or operands having different precisions.

Embodiments described herein enable increased operational throughput fora cluster of compute units by making all logic units within each computeunit available to perform computations. In such embodiments, logic unitswithin a compute unit that are designed to perform computationsselectively at one of multiple precisions or multiple data types can beconfigured to perform multiple simultaneous operations for eachprecision or data type supported by the compute unit. For a givencompute unit 1411-1418, ALUs 1411A-1418A can perform integer operationswhile FPU 1411B-1418B perform floating-point operations. Theseoperations can be performed for a single instruction or for multipleinstruction. In one embodiment a new class of mixed-precisioninstruction is enabled in which one or more operands are of one datatype or precision while one or more different operands are of adifferent data type or precision. For example, an instruction can accepttwo or more multiple-element operands that include floating-point andinteger data types and a single instruction is performed on a per-datatype or per-precision basis.

A Reconfigurable 16-Bit Floating-Point/Integer Fused Multiply-Add Unit

The logic unit designs provided by embodiments described herein havesingle-cycle and multi-cycle latency while maintaining single-cyclethroughput for both fused multiply-add (e.g., 3-operand input with nodependence across cycles) and multiply-accumulate (e.g., 2-operand inputwith data dependence across cycles). In contrast, logic unit designsknown in the art implement fused multiply-add without regard tomulti-cycle latency and single-cycle throughput multiply-accumulateoperations, which may be a limiting factor for performance for keymachine-learning operations, such as the dot product operation.

One embodiment described herein provides for a mergedinteger/floating-point fused multiply-add datapath utilizes the existingsigned integer multiply-add circuits to also accomplish floating-pointmantissa multiply-add operations. With the addition of only the circuitsrequired for alignment/normalization shifts and exponent unit, floatingpoint support is enabled. Input/output data widths and data memoryfootprint remains the same, with only a single control signal requiredto switch on a per-cycle basis between the two compute modes.

One embodiment provides for a merged 16-bit integer/floating-point fusedmultiple-add design that improves upon conventional single-cycle designswith separate integer/floating-point datapaths. The designs describedherein realize multiply-add circuits for a merged int16/float16 datapaththat reduces total area by up to 29%. One embodiment provides for animproved floating-point datapath with alignment only for addend alongwith combined negation and rounding incrementor that contributes to 11%of the total area reduction. One embodiment provides for amultiply-accumulate variant with two inputs and a two-cycle latency,single-cycle throughput. One embodiment provides for an alternativecircuit that significantly increases accumulation accuracy by doublingthe accumulator width at a cost of only 11% in increased area.

FIG. 15A-15B illustrate designs for logic units to perform integer andfloating-point fused multiply-add operations, according to anembodiment. FIG. 15A shows a conventional design for a logic unit 1500that enables a fused multiply-add operation while maintaining fullintermediate product accuracy and range. The fused multiply-addoperation (o=a*b+c) is performed on three 16-bit input operands 1501 ineither IEEE half-precision floating-point (float16) or signed 16binteger (int16) modes. The inputs are provided to either a 16-bitfloating-point datapath 1510 or a 16-bit integer datapath 1520, with theoutput port (o 1530) selecting the appropriate result (f16o 1518 or i16o1528) based on the operation mode 1532. The int16 result (i16o 1528)selects and rounds to nearest the upper half of the 32b signed integerresult (isum 1525) that is generated by a signed 16bx16b multiplier 1521and a 32b adder 1522. The 16-bit floating-point datapath 1510right-shifts (1511) the mantissa of the smaller of the product of anunsigned 11bx11b multiplier 1617 and right shifts the addend foralignment at an alignment shifter 1512A before processing the productvia a 22-bit mantissa adder 1513. A 22-bit leading zero anticipator (LZA1519) predicts the location of the most significant bit location of theresult of the floating-point addition performed by the 22-bit mantissaadder 1513 based on the inputs to the adder. A left-shift (1514) isperformed by a normalization shifter 1515 before the intermediate resultis provided to rounding logic 1516.

FIG. 15B is a block diagram of a multiply-add logic unit 1540, accordingto an embodiment. The logic unit 1540 of FIG. 15B maintains separate16-bit floating-point/integer circuits while improving on thefloating-point datapath of logic unit 1500. In one embodiment the designof logic unit 1540 removes the alignment shifter 1512B from the criticalpath by performing the alignment only on the addend, in parallel withthe multiply operation (1541). The wider 33-bit sum only requires an11-bit incrementor for the upper bits. Additionally, for subtractoperations the output of the adder may be negated to produce an unsignedmantissa. In one embodiment the incrementor is removed from the criticalpath of the datapath of logic unit 1540 by combining the incrementoperation with the final round incrementor (1542). To the contrary,logic unit 1500 of FIG. 15A requires an incrementor to complete anyrequired two's complement negate operations after the adder. Thecritical path reduction with the 16-bit floating-point datapath of logicunit 1540 results in smaller gates and allows an 11% area reductionrelative to logic unit 1500 while maintaining the same single-cyclelatency.

FIG. 16 illustrates fused multiply-add logic unit 1600 having a mergedfloating-point and integer datapath, according to an embodiment. A16-bit×16-bit signed multiplier 1602A and 32-bit adder 1604 of theinteger datapath are reused for floating-point mantissa operations withupper operand bits gated to produce results for 11-bit mantissas(1602B). Input switches 1601A-1601C are used to re-direct the upper6-bits of the input operands (a, b, c) to an exponent unit 1608 whenfloating-point mode is enabled. Sign and exponent values from the inputsare packed and provided to an exponent unit 1608 via a fixed 3-bit signoperand bus 1609A and a 15-bit exponent bus 1609B. For 16-bitfloating-point operations, the shared 32-bit adder uses a 1-bitincrementor 1605 to create the upper bit(s) 1606 of the 33-bit sum.Bypass circuits (1610A, 1610B) within the exponent unit 1608, as well asin the alignment shifter 1612 and normalization shifter 1613, ensurefixed alignment/normalization with minimal switching activity in thoseunits for integer mode, while zero upper mantissa bits ensure noswitching activity within unused portions of the multiplier infloating-point mode. The round logic 1616 and incrementor of thefloating point datapath is re-used for integer mode to compute lower10-bits of integer result i16o with rounding. The upper 6-bits of i16oare computed by mapping that operation onto existing exponentincrementor 1611, which also performs any rounding overflow operationsfrom the mantissa datapath in floating-point mode. A 16-bit floatingpoint or integer value can be provided via an output 1630 whenprocessing is complete.

FIG. 17A illustrates a logic unit 1700 including merged computationcircuits to perform floating point and integer fused-multiply accumulateoperations, according to an embodiment. The logic unit 1700 includes anexponent unit 1708 and a mantissa unit 1709, two 16-bit input ports 1701and a 16-bit output port 1730. The input ports 1701 include switches toswitch sign and exponent bits of the input data to the exponent unit1708. The exponent unit 1708 and the mantissa unit 1709 are used whenperforming integer operations. In one embodiment the logic unit supportsan 8.8 input and 16.0 output format for a 16-bit fixed point mode. Thelogic unit 1700 supports two-cycle latency and single-cycle throughputrequirements. Some of the illustrated circuits are shared betweenoperational modes, including the signed multiplier 1702A-1702B and32-bit adder 1704, which are used for both integer and floating-pointmodes. A 16-bit accumulator input 1703A is asserted during accumulationin the second cycle, where the value of the accumulator is provided tothe 32-bit adder 1704. The upper 10 bits of the accumulator input 1703A(e.g., c[15:6]) are exclusive to 16-bit integer operation. For bothcomputation modes, multiplication is performed in the first cycle andaddition/rounding in the second cycle.

The logic unit 1700 of FIG. 17A uses three key techniques to enable anefficient merged design. First, a straight-forward pipelining of thesingle-cycle merged design of FIG. 16 for accumulate operations wouldeither reduce throughput by half with addend alignment in first cycle orincreases the cycle time with right-shift computation and 33b alignmentin the critical path for second cycle. Instead, the design of logic unit1700 takes advantage of the timing/area non-criticality of the exponentunit 1708 to precompute the bigger (or smaller) mantissa and right-shiftamount for the alignment shifter 1713. In one embodiment the logic unit1700 performs two-cycle operation while maintaining single-cyclethroughput by feeding the output back to the second cycle as addendinput, picking a smaller mantissa for only 22-bit alignment andpre-computing the smaller mantissa/right shift amount in the first cycleusing the multiplier output and the accumulator exponent previouslycomputed by the second stage.

Second, a round to nearest operation in 16-bit integer mode takesadvantage of the 8.8 fixed-point format and eliminates the need to mapinteger rounding onto the floating-point round incrementor. A one isinserted in place of a zero at bit position 15 by the multiplexer logic1705 before the adder to achieve the same rounding operation.

Third, flip-flops are reused for mutually exclusive signals such asexponent computation (e.g., Eun 1707, Rightshift 1710) and upper 10b ofproduct (1711) between the two modes. Timing path reduction in thesecond cycle is also achieved by combining the negation/roundincrementors and by using far/near path based optimizations to reducecritical path through the alignment shifter 1713 and normalizationshifter 1714.

The accuracy of the two-cycle multiply-accumulate design issignificantly increased by doubling the width of only the accumulator to32-bits as shown in FIG. 17B. The accumulator can accumulate 16-bitinteger results in a 16.16 fixed point format and 16-bit floating-pointresults based on an intermediate result having a 5-bit exponent and a22-bit mantissa (implicit leading 1 not stored). The 22-bit mantissa ofthe intermediate result, in various embodiments, can be rounded,truncated, or quantized to an IEEE standard mantissa. The design oflogic unit 1740 limits the cost of the doubled accumulator primarily tothe output flip-flops and final incrementor in the mantissa datapath, asthe remaining datapath after the multiplier already accommodates theadditional width for the product. In one embodiment the higher accuracyenables simplifying the rounding to a simple truncation to generate a16-bit output 1750 from the 32-bit accumulator. The post exponentnormalization incrementor is removed from the exponent unit 1708 inlogic unit 1740. Instead, a negation incrementor 1742 performs a finalincrement in the mantissa to compute the two's complement when theoutput of the adder is to be negated. A 32-bit accumulator input 1703Bis asserted during accumulation in the second cycle, where the value ofthe accumulator is provided to the 32-bit adder 1704. The upper 10 bitsof the accumulator input 1703B (e.g., c[31:22]) are exclusive to 16-bitinteger operation. Synthesized total area of this design presents onlyan 11% area increase relative to the design of logic unit 1700 of FIG.17A while doubling accumulator precision.

Even though the above descriptions are provided for 16-bit operands,these techniques can be easily extended to larger data widths to achievesimilar goals. Additionally, while IEEE half precision output isdescribed, the designs described herein may also be adjusted to supportnon-standard floating point formats. Additionally, differentnon-standard floating-point formats may be used for intermediate values,as described below.

Embodiments described above provide various implementations of areconfigurable 16-bit Floating-Point/Integer Fused Multiply-Add Unitthat provides multiple advantages over existing designs. The proposeddesign does not affect memory footprint for floating-point or integernumber storage. The proposed designs only increase multiplier areawithout changing the remainder of the floating-point datapath. Incontrast, logic designs known in the art extend the entire floatingpoint significand/mantissa to the same width as the integer number,while additional storage area for sign and exponent is separate andexclusive only to floating-point numbers, resulting in an increase inregister file size and footprint for floating-point number storage.Existing designs also increase width of the entire mantissa datapath,which can result in significant area increase. Single cycle (e.g., logicunit 1600 of FIG. 16) and multi-cycle (e.g., logic unit 1700 of FIG. 17Aand logic unit 1740 of FIG. 17B) designs are provided, where themulti-cycle, after an initial latency, generate an output each cycle.Logic unit 1740 of FIG. 17B provides a merged floating-point/integermultiply-accumulate design with a local accumulator width that is twiceas wide as the input operands. This enables much higher accumulationaccuracy for operations like dot-products without impacting memorystorage footprint of input operands and affects a small portion of thedesign for only 11% total area impact. Furthermore, each logic unit mapsa portion of the integer operation onto the existing exponent datapathto maximize circuit re-use when reconfiguring for integer mode.Additionally for floating-point operations with subtract operations, thelogic unit 1540 of FIG. 15B and 1700 of FIG. 17A combine the 2'scomplement increment into the round increment for reduced delay andarea.

Machine Learning Data Processing System and Acceleration Logic

One embodiment uses the multiprocessor unit 1400 of FIG. 14 and one ormore floating-point/integer logic units of FIG. 15A-17B can be used asbuilding blocks for machine learning data processing system thatincludes hardware, software, and firmware that is optimized to performthe type of compute operations commonly performed when performingtraining or inferencing using deep neural networks. FIG. 18A-18Billustrate a data processing system and associated compute and logicunits that to perform accelerated training and inferencing operationsfor machine learning, for example, via the use of deep neural networks.FIG. 18A illustrates an exemplary machine learning data processingsystem provided by embodiments described herein. FIG. 18B illustratescomponents of a machine learning accelerator, according to oneembodiment.

The data processing system 1800 of FIG. 18A is a heterogeneousprocessing system having a processor 1802, unified memory 1810, and aGPGPU 1820 including machine learning acceleration logic. The processor1802 and the GPGPU 1820 can be any of the processors and GPGPU/parallelprocessors as described herein. The processor 1802 can executeinstructions for a compiler 1815 stored in system memory 1812. Thecompiler 1815 executes on the processor 1802 to compile source code1814A into compiled code 1814B. The compiled code 1814B can include codethat may be executed by the processor 1802 and/or code that may beexecuted by the GPGPU 1820. During compilation, the compiler 1815 canperform operations to insert metadata, including hints as to the levelof data parallelism present in the compiled code 1814B and/or hintsregarding the data locality associated with threads to be dispatchedbased on the compiled code 1814B. The compiler 1815 can include theinformation necessary to perform such operations or the operations canbe performed with the assistance of a runtime library 1816. The runtimelibrary 1816 can also facilitate the compiler 1815 in the compilation ofthe source code 1814A and can also include instructions that are linkedat runtime with the compiled code 1814B to facilitate execution of thecompiled instructions on the GPGPU 1820.

The unified memory 1810 represents a unified address space that may beaccessed by the processor 1802 and the GPGPU 1820. The unified memoryincludes system memory 1812 as well as GPGPU memory 1818. The GPGPUmemory 1818 includes GPGPU local memory 1834A-1834B within the GPGPU1820 and can also include some or all of system memory 1812. Forexample, compiled code 1814B stored in system memory 1812 can also bemapped into GPGPU memory 1818 for access by the GPGPU 1820.

The GPGPU 1820 includes multiple compute blocks 1824A-1824N, which canbe instances of the processing cluster 214A-214N of FIG. 2A and caninclude one or more instances of the graphics multiprocessor 234described herein. In various embodiments, the compute blocks 1824A-1824Ninclude compute units having one or more of the logic units of FIG.15B-17B. The GPGPU 1820 also includes a set of registers 1825, cachememory 1827, and a power and performance module 1826 that can be used asshared resources for the compute blocks 1824A-1824N. In one embodimentthe registers 1825 include directly and indirectly accessible registers,where the indirectly accessible registers may be optimized for use inmatrix compute operations. The power and performance module 1826 can beconfigured to adjust power delivery and clock frequencies for thecompute blocks 1824A-1824N to power gate idle components within thecompute blocks 1824A-1824N under heavy workloads. The GPGPU 1820includes GPGPU local memory 1828, which are physical memory modules thatshare a graphics card or multi-chip module with the GPGPU 1820.

In one embodiment the GPGPU 1820 includes hardware logic including afetch and decode unit 1821, a scheduler controller 1822, and a machinelearning accelerator 1823. The instruction fetch and decode unit 1821 isa fetch and decode unit including logic to fetch and decodeinstructions, including machine learning specific instructions, that candefine complex, customizable behavior. The instructions can cause thecompute logic to schedule, via the scheduler controller 1822, a set ofoperations to be performed via one or more of the compute blocks1824A-1824N. In one embodiment the scheduler controller 1822 is an ASICconfigurable to perform advanced scheduling operations. In oneembodiment the scheduler controller 1822 is a micro-controller or a lowenergy-per-instruction processing core capable of performinginstructions loaded from a firmware module.

In one embodiment some functions to be performed by the compute blocks1824A-1824N can be directly scheduled to or offloaded to the machinelearning accelerator 1823. The machine learning accelerator 1823includes processing element logic configured to efficiently performmatrix and other compute operations on commonly performed during machinelearning.

In some embodiments the GPGPU 1820 additionally incudes a statisticsunit 1829 that may be configured as a near-data compute unit. Forexample, the statistics unit 1829 may be integrated into or spreadacross one or more memory controllers for the GPGPU local memory 1828.In one embodiment the statistics unit 1829, when enabled by the machinelearning accelerator 1823, can be used to determine a probabilitydistribution for weight or activation map data when performingmachine-learning operations that write to or read from the GPGPU localmemory 1828. The statistics unit 1829 includes logic to determine, basedon address and data patterns during memory access, whether data that isaccessed in the GPGPU local memory 1828 fits within one or morestatistical distributions (e.g., Gaussian, uniform, Poisson etc.). Inone embodiment, statistical information (e.g., mean, median, mode,standard deviation, etc.) can be gathered during sample period for atleast a subset of memory accesses. The statistics unit 1829 can beconfigured such that gathering the statistical information does notsignificantly increase the latency of memory accesses performed througha memory controller hosting the statistics unit 1829. The statisticalinformation can be periodically provided to the machine learningaccelerator 1823 or the machine learning accelerator 1823 can requestthe data from the statistics unit. In one embodiment the statistics unit1829 can check data associated with memory accesses against a set ofknown-likely distributions. A vector including a set of probabilitiesassociated with each of the known-likely distributions can be providedto the machine learning accelerator 1823 on a periodic basis or uponrequest. In various embodiments the machine learning accelerator 1823can use the probability and/or statistical information provided by thestatistics unit 1829 for a variety of operation. In one embodiment, asdescribed further in FIG. 18B and FIG. 20, the machine learningaccelerator 1823 can use data provided by the statistics unit 1829 toperform stochastic rounding during quantization for low precision neuralnetworks.

The machine learning accelerator 1823 of FIG. 18A is illustrated infurther detail in FIG. 18B. In one embodiment the machine learningaccelerator 1823 includes an activation instruction module 1832, an FPUencoding and configuration module 1834, a stochastic quantization unit1838, and a cache memory 1836 that is shared between the various moduleswithin the machine learning accelerator 1823.

The activation instruction module 1832 includes logic to sequence theperformance of a combined fused multiply-add and activation in responseto a single instruction. In response to decode of a FMAC or FMADD plusactivation function on the GPGPU 1820, the scheduler controller 1822 canschedule an operation via the machine learning accelerator 1823. Themachine learning accelerator 1823, via the activation instruction module1832, can perform a set of fused multiply-add or fusedmultiply-accumulate operations on two or three input operands per threador vector element and, for each thread or element, provide the output tohardware logic configure to perform one of multiple selectableactivation functions. A different activation function can be associatedwith different instructions or a single instruction can include a fieldto enable selection of an activation function. In one embodiment, theactivation instruction module can perform a vector or warp operation togenerate intermediate FMADD or FMAC result and store the intermediateresults in the cache memory 1836. The activation instruction module 1832can then apply the activation function to the intermediate data.Exemplary supported activation functions include the rectified linearunit (RELU) function of equation (1), the sigmoid function of equation(2), or the hard-sigmoid function of equation (3).

$\begin{matrix}{{f(x)} = {\max\left( {0,x} \right)}} & (1)\end{matrix}$ $\begin{matrix}{{\sigma(x)} = \frac{1}{\left( {1 + e^{- x}} \right)}} & (2)\end{matrix}$ $\begin{matrix}{{\sigma(x)} = {\max\left( {0,{\min\left( {1,\frac{x + 1}{2}} \right)}} \right)}} & (3)\end{matrix}$

The FPU encoding and configuration module 1834 includes logic to defineparameters for the dynamic configuration of floating point units withinthe compute blocks 1824A-1824N of the GPGPU 1820. In one embodimentcertain dynamic aspects of the merged integer/floating-point units ofFIG. 16 and FIG. 17A-17B can be configured via the FPU encoding andconfiguration module 1834. For example, the compute blocks 1825A-1824Ncan be overprovisioned to contain more compute units than can bemaximally active at any one time given the power budget of the GPGPU1820. However, the FPU encoding and configuration module 1834 canconfigure the dynamic floating point units to gate certain logic blocksto operate at reduce precision and reduced power draw. The reducedprecision and power requirements of each unit can enable a larger numberof units to be online, allowing a larger number of threads to beperformed for lower-precision operations. For example, in one embodimentlogic units that may be configure to perform 16-bit integer operationscan be configured to perform 8-bit integer operations, reducing powerrequirements. In one embodiment, dual 8-bit integer operations can beperformed, increasing throughout without significantly increasing powerdraw. In one embodiment, multiple half-precision logic units can work inparallel to perform single precision or double precision floating pointoperations. In one embodiment, higher precision operations can beperformed via multiple passes through the logic unit.

In one embodiment the FPU encoding and configuration module 1834 canalso configure the floating-point encoding methods supported by thefloating point units. In addition to IEEE 754 floating-point standardsfor half, single, and double precision encoding for floating pointvalues, a myriad of alternative encoding formats may be supported basedon the dynamic range of the data that is currently being processed. Forexample, based on the dynamic range and/or distribution a given dataset,the data may be quantized more accurately from higher to lower precisionby using greater than or fewer bits for exponent or mantissa data. Inone embodiment a supported alternative format specifies a 1-bit sign,with an 8-bit exponent and a 7-bit fractional component. One embodimentallows encoding with a 1-bit sign, a 3-bit exponent, and a 12-bitfractional component. In such embodiments, differing sets ofinstructions support different floating point encodings, allowing adeveloper to select an encoding based on the instruction specified inprogram code. In one embodiment, differing floating point encodings canbe used when rounding or down sampling floating-point data, for example,from an accumulated 32-bit floating-point value to a 16-bit value. Inone embodiment the statistics unit 1829 can be leveraged to determinewhich 16-bit encoding is best suited for a given block of data.

In one embodiment the machine learning accelerator 1823 additionallyincludes stochastic quantization unit 1838 to enable stochasticquantization for machine learning operations. The stochasticquantization unit 1838 can be used to enable stochastic rounding duringquantization operations. One embodiment enables stochastic roundingusing a random number generator, where a fractional value can be used todetermine a rounding probability. One embodiment makes use of thestatistics unit 1829 to determine a probability distribution associatedwith the set of output data from a given layer of a neural network. Foreach layer, a probability density of the data values can be determined,where the probability density is determined by statisticalcharacteristics including the mean, standard deviation, and variance ofthe data determined for each layer of the neural network. Using suchdata, stochastic rounding can be performed in a manner that does notalter the probability distribution of the data within each layer of theneural network.

FIG. 19 illustrates details of the activation instruction module 1832,according to an embodiment. The activation instruction module 1832includes logic to sequence the performance of a combined fusedmultiply-add and activation in response to a single instruction. Inresponse to decode of a FMAC/FMADD+ Activation function by theinstruction fetch and decode unit 1821 of FIG. 18A, instructionexecution can be dispatched to the activation instruction module 1832via the machine learning accelerator 1823. The machine learningaccelerator 1823, upon receipt of the instruction, can use a fusedmultiply-add/fused multiply-accumulate thread scheduler unit 1902 toschedule a set of fused multiply-add or fused multiply-accumulateoperations to compute units within compute blocks 1824A-1824N. In oneembodiment, intermediate data output from the compute blocks 1824A-1824Ncan be stored in cache memory 1836 within the machine learningaccelerator 1823. In one embodiment, chunks of intermediate data can beprocessed in a streaming manner within the activation instruction module1832. The intermediate data, in one embodiment, can represent anactivation map to which the non-linearity of the activation functionwill be applied. A selected one of the activation functions can beapplied by activation function logic 1904A-1904N. The activationfunction can be selected based on the specific instruction processed bythe activation instruction module 1832 or parameters supplied with theinstruction. The specific instruction can be formatted based on any ofthe instruction formats described herein.

Floating-point operations, at various points, include a roundingoperation. Rounding is used in floating point calculations becausefloating point numbers have a limited number of digits and cannotrepresent all real numbers accurately. Thus, when a number is tasked torepresent a value that requires more digits than the selected floatingpoint format allows, the leftover digits are omitted and the number isrounded to the nearest value that may be represented by thefloating-point format. The specific numbers that may be represented isdependent upon the floating-point format selected.

Various approaches to rounding during floating point calculations may beperformed. Embodiments described herein include hardware logic toperform stochastic rounding for machine learning operations. In contrastwith other rounding approaches that round to the nearest number orstrictly up and down, the stochastic approach rounds numbers randomly.Embodiments described herein enable stochastic rounding for quantizationof data values for deep neural networks. A rounding unit is providedthat enables hardware stochastic rounding using one of multiple roundingmodes. One embodiment enables stochastic rounding using a random numbergenerator. A fractional value can be used to determine a roundingprobability. The random number can be compared with the roundingprobability to determine which of the nearest representable value toround during quantization. Alternatively, one embodiment makes use ofstatistics accumulator/estimator logic to determine a probabilitydistribution associated with the set of output data from a given layerof a neural network. For each layer, a probability density of thedistribution of the data values can be determined, where the probabilitydensity is defined by the mean, standard deviation, and variance of thedata determined for each layer of the neural network. Using such data,stochastic rounding can be performed in a manner that does not alter theprobability distribution for each layer of the neural network.

FIG. 20 illustrates the stochastic quantization unit 1838, according toan embodiment. In one embodiment the stochastic quantization unit 1838is used to quantize raw output data generated within a layer of a neuralnetwork into the format used by the next layer of the neural network.For example, the computation operations used to generate output data maybe processed at higher precision and the results may be quantized to alower precision before being provided as input to the next layer. In oneembodiment, the output 2002B from a given layer n is processed, forexample, at 32-bits and quantized by the quantization unit 2004 into a16-bit data type. The quantization operation can make use of stochasticrounding, which may be implemented via a stochastic rounding unit 2009.The quantized and rounded values can then be provided to the next layer(Layer N+1) 2010 of the neural network.

In various embodiments the stochastic quantization unit 1838 can performstochastic rounding via the use of a random number generator 2006. Infloating-point arithmetic, rounding aims to turn a given value x into avalue z with a specified number of significant digits, where, z is amultiple of a number m that depends on the magnitude of x. The number mis a power of the base (usually 2 or 10) of the floating-pointrepresentation. The number z is a representable value that is proximateto the value x. Whether the value x is rounded up or down to realize thevalue z is based on a random value that is selected by the random numbergenerator 2006. The random value that is generated is compared with thefractional portion between valid representations. The fractional portioncan be used as the probability of rounding up or down to the nearestrepresentable value. The gap between the representable values duringquantization depends on the encoding format for the floating-pointrepresentation in place. As an example, if the quantization is to roundto an integer value and the fractional value is 0.3, the probability ofrounding up can be equated to 30%, while the probability of roundingdown can be equated to 70%. In such scenario, where the random numbergenerator 2006 is a properly validated, true random number generator,the stochastic rounding unit 2009 will round up or down in proportion tothe fractional value.

Alternatively, the stochastic rounding unit 2009 can make use of astatistics accumulator/estimator 2008, which, in one embodiment, is anear-data statistics unit 1829 as in FIG. 18A. The statisticsaccumulator/estimator 2008 can analyze output from previous layers2002A-2002B to determine the distribution associated with the neuralnetwork data. The stochastic rounding unit 2009 can then round dataduring quantization such that the quantized data has a similardistribution as the pre-quantized data.

FIG. 21 illustrates the FPU encoding and configuration module 1834,according to one embodiment. In one embodiment the GPU encoding andconfiguration module 1834 includes an FPU configuration module 2102 andan FPU encoding module 2104. The FPU configuration module 2102 can beused to configure 16-bit integer logic units to perform 8-bit integeroperations, including dual 8-bit integer operations. In one embodimentmultiple half-precision logic units can work in parallel to performsingle precision or double precision floating point operations. The FPUencoding module 2104 can be used to configure the specificfloating-point encoding format to use within the computation blocks1824A-1824N during data computations. In one embodiment the FPU encodingmodule 2104 can configure one or more of the compute blocks 1824A-1824Nin response to an instruction that specifies that input or output datais to be stored in a non-standard floating-point format. The computeblocks to execute the instruction can then be configured to interpretdata in the non-standard format before operations of the instructionsare executed. In one embodiment the FPU encoding module 2104 is toconfigure one or more of the compute blocks to use a floating-pointencoding format that can most efficiently store the data to beprocessed. Such determination can be performed in part based onprobability and statistics information provided by the statistics unit1829, which can function as a near-data compute unit that is situatedwithin a memory controller 2106 of the GPGPU local memory 1828.

FIG. 22 illustrates logic 2200 to process an instruction using adynamically configurable compute unit, according to an embodiment. Thelogic 2200 can be hardware or firmware logic within a GPGPU and/or GPGPUmultiprocessor as described herein, such as the multiprocessor unit 1400as in FIG. 14, or the GPGPU 1820 of FIG. 18A. The logic 2200 isconfigured to fetch and decode a single instruction to perform acombined multiply-add operation on a set of operands, as shown at block2202. The logic 2200 can then issue the single instruction for executionby a compute unit for execution by dynamically configurable computeunit, as shown at block 2204. The logic 2200 can then configure one ormore logic units of the compute unit to perform operations at theprecision and data type of the operands, as shown at block 2206. Thelogic 2200 can then execute the single instruction at the compute unitto generate an output based on a multiply and add operation, as shown atblock 2208.

In one embodiment the combined multiply and add operation performed atblock 2202 can be a fused floating-point operation including a singlerounding. For example, the multiply and add operation can be a fusedmultiply-add or a fused multiply-accumulate operation. The combinedmultiply and add operation can also be an integer operation. The integeroperation can include a round operation between the multiply and theadd. The round can be performed by inserting a zero at the highest bitposition of the integer data type via a multiplexer within the logicunit. The multiplexer can be positioned after the multiplier and beforethe adder within the logic unit.

In one embodiment the dynamically configurable logic unit of block 2204is a merged floating-point and integer logic unit that is configurableto perform integer or floating-point operations. For example, thedynamically configurable logic unit may be one of logic unit 1600 ofFIG. 16, 1700 of FIG. 17A, or 1740 of FIG. 17B. The compute unit caninclude multiple different instances of such logic units. In oneembodiment the logic unit is configurable on a per-cycle basis. In oneembodiment the logic unit is a first logic unit configured to perform asingle-cycle fused-multiply add operation using a multiplier and anadder that are shared between floating-point and integer datapaths. Inone embodiment the logic unit is a second logic unit configured toperform a two-cycle fused multiply accumulate operation havingsingle-cycle throughput. In one embodiment the logic unit is a thirdlogic unit configured to perform a two-cycle fused multiply accumulateoperation, where the third logic includes an accumulator having twicethe bit-width of the input and output operands. In one embodiment thedie area of the third logic unit is at most eleven percent greater thanthe die area of the second logic unit.

The dynamically configurable logic units described herein can beconfigured to perform integer or floating-point operations. In oneembodiment one or more of the logic units can be configured to performoperations at multiple different precisions. In one embodiment the logicunits can be used to perform operations at multiple different precisionsvia multi-cycle operations. In one embodiment, different floating-pointencodings may be selected, including the IEEE 754 half-precisionfloating-point format, single precision floating-point format, anddouble precision floating-point format. Non-standard floating pointformats may also be used in which different bit allocations are used forthe exponent and mantissa of the floating-point values.

In one embodiment the output based on the multiply and add operation canthen be additionally processed by an activation function. For example,in response to a single instruction, an FMADD or FMAC operation can bescheduled by an FMADD/FMAC thread scheduler unit, as shown in FIG. 19.The output of such operations may be activation map data that can beprovided to activation function logic (e.g., activation function logic1904 as in FIG. 19) to generate neuron activation data.

FIG. 23A illustrates logic 2300 to execute a machine learninginstruction, according to an embodiment. The logic 2300 can be hardwareor firmware logic within a GPGPU and/or GPGPU multiprocessor asdescribed herein, such as the multiprocessor unit 1400 as in FIG. 14, orthe GPGPU 1820 of FIG. 18A. The logic 2300 is configured to fetch anddecode a single instruction to perform a set of machine learningoperations via a machine learning accelerator unit, as shown at block2302. The machine learning accelerator unit includes element of themachine learning accelerator 1823 described herein, including theactivation instruction module 1832, FPU encoding and configurationmodule 1834, and stochastic quantization unit 1838 of FIG. 18B. Thelogic 2300 can then issue the single instruction for execution by a setof dynamically configurable compute units, as shown at block 2304. Thelogic can then configure the set of compute units to perform the set ofmachine learning operations at a higher precision than the inputs andoutputs of the operations, as shown at block 2306. In one embodiment theconfiguration is performed by an FPU configuration module as describedherein. The FPU configuration module can configure the compute units toperform, for example, a convolution operation on 16-bit floating pointmatrix data using 32-bit intermediate data. As shown at block 2308, thelogic 2300 can then quantize the higher precision intermediate values toa lower precision before output via stochastic rounding logic within themachine learning accelerator. For example, 32-bit intermediate data canbe quantized to 16-bits for output using stochastic rounding.

FIG. 23B illustrates logic 2310 to configure floating-point operationsbased on a distribution of neural network data, according to anembodiment. In one embodiment the logic 2300 includes hardware andfirmware logic and logic units described herein, including a stochasticquantization unit 1838 of FIG. 18B and FIG. 20, the FPU encoding andconfiguration module 1834 of FIG. 18B. The statisticsaccumulator/estimator 2008 of FIG. 20, in one embodiment, is includedwithin the statistics unit 1829 of FIG. 18A. The statistics unit 1829may be a near-data compute unit included within a memory controller forthe GPGPU, as shown in FIG. 21.

Using the statistics unit, the logic 2310 can determine a set ofstatistical metrics for neural network data stored in memory, as shownat block 2312. The logic 2310 can then determine, via the statisticalmetrics, a distribution for the neural network data in memory, as shownat block 2314. In one embodiment the logic 2310 can configurefloating-point encoding for the compute units for use in performing aset of machine learning operations, as shown at block 2316. The logic2310 can then configure stochastic rounding logic within the machinelearning accelerator to round based on the distribution, as shown atblock 2318. The stochastic rounding logic can be configured to roundbased on the distribution such that the probability distribution of thequantized neural network data is closer to the pre-quantized data thanmay be possible using random number generator based stochastic roundingtechniques.

Additional Exemplary Graphics Processing System

Details of the embodiments described above can be incorporated withingraphics processing systems and devices described below. The graphicsprocessing system and devices of FIG. 24 through FIG. 37 illustratealternative systems and graphics processing hardware that can implementany and all of the techniques described above.

Additional Exemplary Graphics Processing System Overview

FIG. 24 is a block diagram of a processing system 2400, according to anembodiment. In various embodiments the system 2400 includes one or moreprocessors 2402 and one or more graphics processors 2408, and may be asingle processor desktop system, a multiprocessor workstation system, ora server system having a large number of processors 2402 or processorcores 2407. In one embodiment, the system 2400 is a processing platformincorporated within a system-on-a-chip (SoC) integrated circuit for usein mobile, handheld, or embedded devices.

An embodiment of system 2400 can include, or be incorporated within aserver-based gaming platform, a game console, including a game and mediaconsole, a mobile gaming console, a handheld game console, or an onlinegame console. In some embodiments system 2400 is a mobile phone, smartphone, tablet computing device or mobile Internet device. Dataprocessing system 2400 can also include, couple with, or be integratedwithin a wearable device, such as a smart watch wearable device, smarteyewear device, augmented reality device, or virtual reality device. Insome embodiments, data processing system 2400 is a television or set topbox device having one or more processors 2402 and a graphical interfacegenerated by one or more graphics processors 2408.

In some embodiments, the one or more processors 2402 each include one ormore processor cores 2407 to process instructions which, when executed,perform operations for system and user software. In some embodiments,each of the one or more processor cores 2407 is configured to process aspecific instruction set 2409. In some embodiments, instruction set 2409may facilitate Complex Instruction Set Computing (CISC), ReducedInstruction Set Computing (RISC), or computing via a Very LongInstruction Word (VLIW). Multiple processor cores 2407 may each processa different instruction set 2409, which may include instructions tofacilitate the emulation of other instruction sets. Processor core 2407may also include other processing devices, such a Digital SignalProcessor (DSP).

In some embodiments, the processor 2402 includes cache memory 2404.Depending on the architecture, the processor 2402 can have a singleinternal cache or multiple levels of internal cache. In someembodiments, the cache memory is shared among various components of theprocessor 2402. In some embodiments, the processor 2402 also uses anexternal cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC))(not shown), which may be shared among processor cores 2407 using knowncache coherency techniques. A register file 2406 is additionallyincluded in processor 2402 which may include different types ofregisters for storing different types of data (e.g., integer registers,floating point registers, status registers, and an instruction pointerregister). Some registers may be general-purpose registers, while otherregisters may be specific to the design of the processor 2402.

In some embodiments, processor 2402 is coupled with a processor bus 2410to transmit communication signals such as address, data, or controlsignals between processor 2402 and other components in system 2400. Inone embodiment the system 2400 uses an exemplary ‘hub’ systemarchitecture, including a memory controller hub 2416 and an Input Output(I/O) controller hub 2430. A memory controller hub 2416 facilitatescommunication between a memory device and other components of system2400, while an I/O Controller Hub (ICH) 2430 provides connections to I/Odevices via a local I/O bus. In one embodiment, the logic of the memorycontroller hub 2416 is integrated within the processor.

Memory device 2420 can be a dynamic random access memory (DRAM) device,a static random access memory (SRAM) device, flash memory device,phase-change memory device, or some other memory device having suitableperformance to serve as process memory. In one embodiment the memorydevice 2420 can operate as system memory for the system 2400, to storedata 2422 and instructions 2421 for use when the one or more processors2402 executes an application or process. Memory controller hub 2416 alsocouples with an optional external graphics processor 2412, which maycommunicate with the one or more graphics processors 2408 in processors2402 to perform graphics and media operations.

In some embodiments, ICH 2430 enables peripherals to connect to memorydevice 2420 and processor 2402 via a high-speed I/O bus. The I/Operipherals include, but are not limited to, an audio controller 2446, afirmware interface 2428, a wireless transceiver 2426 (e.g., Wi-Fi,Bluetooth), a data storage device 2424 (e.g., hard disk drive, flashmemory, etc.), and a legacy I/O controller 2440 for coupling legacy(e.g., Personal System 2 (PS/2)) devices to the system. One or moreUniversal Serial Bus (USB) controllers 2442 connect input devices, suchas keyboard and mouse 2444 combinations. A network controller 2434 mayalso couple with ICH 2430. In some embodiments, a high-performancenetwork controller (not shown) couples with processor bus 2410. It willbe appreciated that the system 2400 shown is exemplary and not limiting,as other types of data processing systems that are differentlyconfigured may also be used. For example, the I/O controller hub 2430may be integrated within the one or more processor 2402, or the memorycontroller hub 2416 and I/O controller hub 2430 may be integrated into adiscreet external graphics processor, such as the external graphicsprocessor 2412.

FIG. 25 is a block diagram of an embodiment of a processor 2500 havingone or more processor cores 2502A-2502N, an integrated memory controller2514, and an integrated graphics processor 2508. Those elements of FIG.25 having the same reference numbers (or names) as the elements of anyother figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such. Processor2500 can include additional cores up to and including additional core2502N represented by the dashed lined boxes. Each of processor cores2502A-2502N includes one or more internal cache units 2504A-2504N. Insome embodiments each processor core also has access to one or moreshared cached units 2506.

The internal cache units 2504A-2504N and shared cache units 2506represent a cache memory hierarchy within the processor 2500. The cachememory hierarchy may include at least one level of instruction and datacache within each processor core and one or more levels of sharedmid-level cache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), orother levels of cache, where the highest level of cache before externalmemory is classified as the LLC. In some embodiments, cache coherencylogic maintains coherency between the various cache units 2506 and2504A-2504N.

In some embodiments, processor 2500 may also include a set of one ormore bus controller units 2516 and a system agent core 2510. The one ormore bus controller units 2516 manage a set of peripheral buses, such asone or more Peripheral Component Interconnect buses (e.g., PCI, PCIExpress). System agent core 2510 provides management functionality forthe various processor components. In some embodiments, system agent core2510 includes one or more integrated memory controllers 2514 to manageaccess to various external memory devices (not shown).

In some embodiments, one or more of the processor cores 2502A-2502Ninclude support for simultaneous multi-threading. In such embodiment,the system agent core 2510 includes components for coordinating andoperating cores 2502A-2502N during multi-threaded processing. Systemagent core 2510 may additionally include a power control unit (PCU),which includes logic and components to regulate the power state ofprocessor cores 2502A-2502N and graphics processor 2508.

In some embodiments, processor 2500 additionally includes graphicsprocessor 2508 to execute graphics processing operations. In someembodiments, the graphics processor 2508 couples with the set of sharedcache units 2506, and the system agent core 2510, including the one ormore integrated memory controllers 2514. In some embodiments, a displaycontroller 2511 is coupled with the graphics processor 2508 to drivegraphics processor output to one or more coupled displays. In someembodiments, display controller 2511 may be a separate module coupledwith the graphics processor via at least one interconnect, or may beintegrated within the graphics processor 2508 or system agent core 2510.

In some embodiments, a ring-based interconnect 2512 is used to couplethe internal components of the processor 2500. However, an alternativeinterconnect unit may be used, such as a point-to-point interconnect, aswitched interconnect, or other techniques, including techniques wellknown in the art. In some embodiments, graphics processor 2508 coupleswith the ring-based interconnect 2512 via an I/O link 2513.

The exemplary I/O link 2513 represents at least one of multiplevarieties of I/O interconnects, including an on package I/O interconnectwhich facilitates communication between various processor components anda high-performance embedded memory module 2518, such as an eDRAM module.In some embodiments, each of the processor cores 2502A-2502N andgraphics processor 2508 use embedded memory modules 2518 as a sharedLast Level Cache.

In some embodiments, processor cores 2502A-2502N are homogenous coresexecuting the same instruction set architecture. In another embodiment,processor cores 2502A-2502N are heterogeneous in terms of instructionset architecture (ISA), where one or more of processor cores 2502A-2502Nexecute a first instruction set, while at least one of the other coresexecutes a subset of the first instruction set or a differentinstruction set. In one embodiment processor cores 2502A-2502N areheterogeneous in terms of microarchitecture, where one or more coreshaving a relatively higher power consumption couple with one or morepower cores having a lower power consumption. Additionally, processor2500 can be implemented on one or more chips or as an SoC integratedcircuit having the illustrated components, in addition to othercomponents.

FIG. 26 is a block diagram of a graphics processor 2600, which may be adiscrete graphics processing unit, or may be a graphics processorintegrated with a plurality of processing cores. In some embodiments,the graphics processor communicates via a memory mapped I/O interface toregisters on the graphics processor and with commands placed into theprocessor memory. In some embodiments, graphics processor 2600 includesa memory interface 2614 to access memory. Memory interface 2614 can bean interface to local memory, one or more internal caches, one or moreshared external caches, and/or to system memory.

In some embodiments, graphics processor 2600 also includes a displaycontroller 2602 to drive display output data to a display device 2620.Display controller 2602 includes hardware for one or more overlay planesfor the display and composition of multiple layers of video or userinterface elements. In some embodiments, graphics processor 2600includes a video codec engine 2606 to encode, decode, or transcode mediato, from, or between one or more media encoding formats, including, butnot limited to Moving Picture Experts Group (MPEG) formats such asMPEG-2, Advanced Video Coding (AVC) formats such as H.264/MPEG-4 AVC, aswell as the Society of Motion Picture & Television Engineers (SMPTE)421M/VC-1, and Joint Photographic Experts Group (JPEG) formats such asJPEG, and Motion JPEG (MJPEG) formats.

In some embodiments, graphics processor 2600 includes a block imagetransfer (BLIT) engine 2604 to perform two-dimensional (2D) rasterizeroperations including, for example, bit-boundary block transfers.However, in one embodiment, 2D graphics operations are performed usingone or more components of graphics processing engine (GPE) 2610. In someembodiments, GPE 2610 is a compute engine for performing graphicsoperations, including three-dimensional (3D) graphics operations andmedia operations.

In some embodiments, GPE 310 includes a 3D pipeline 2612 for performing3D operations, such as rendering three-dimensional images and scenesusing processing functions that act upon 3D primitive shapes (e.g.,rectangle, triangle, etc.). The 3D pipeline 2612 includes programmableand fixed function elements that perform various tasks within theelement and/or spawn execution threads to a 3D/Media sub-system 2615.While 3D pipeline 2612 can be used to perform media operations, anembodiment of GPE 2610 also includes a media pipeline 2616 that isspecifically used to perform media operations, such as videopost-processing and image enhancement.

In some embodiments, media pipeline 2616 includes fixed function orprogrammable logic units to perform one or more specialized mediaoperations, such as video decode acceleration, video de-interlacing, andvideo encode acceleration in place of, or on behalf of video codecengine 2606. In some embodiments, media pipeline 2616 additionallyincludes a thread spawning unit to spawn threads for execution on3D/Media sub-system 2615. The spawned threads perform computations forthe media operations on one or more graphics execution units included in3D/Media sub-system 2615.

In some embodiments, 3D/Media sub-system 2615 includes logic forexecuting threads spawned by 3D pipeline 2612 and media pipeline 2616.In one embodiment, the pipelines send thread execution requests to3D/Media sub-system 2615, which includes thread dispatch logic forarbitrating and dispatching the various requests to available threadexecution resources. The execution resources include an array ofgraphics execution units to process the 3D and media threads. In someembodiments, 3D/Media sub-system 2615 includes one or more internalcaches for thread instructions and data. In some embodiments, thesubsystem also includes shared memory, including registers andaddressable memory, to share data between threads and to store outputdata.

Additional Exemplary Graphics Processing Engine

FIG. 27 is a block diagram of a graphics processing engine 2710 of agraphics processor in accordance with some embodiments. In oneembodiment, the graphics processing engine (GPE) 2710 is a version ofthe GPE 2610 shown in FIG. 26. Elements of FIG. 27 having the samereference numbers (or names) as the elements of any other figure hereincan operate or function in any manner similar to that describedelsewhere herein, but are not limited to such. For example, the 3Dpipeline 2612 and media pipeline 2616 of FIG. 26 are illustrated. Themedia pipeline 2616 is optional in some embodiments of the GPE 2710 andmay not be explicitly included within the GPE 2710. For example and inat least one embodiment, a separate media and/or image processor iscoupled to the GPE 2710.

In some embodiments, GPE 2710 couples with or includes a commandstreamer 2703, which provides a command stream to the 3D pipeline 2612and/or media pipelines 2616. In some embodiments, command streamer 2703is coupled with memory, which can be system memory, or one or more ofinternal cache memory and shared cache memory. In some embodiments,command streamer 2703 receives commands from the memory and sends thecommands to 3D pipeline 2612 and/or media pipeline 2616. The commandsare directives fetched from a ring buffer, which stores commands for the3D pipeline 2612 and media pipeline 2616. In one embodiment, the ringbuffer can additionally include batch command buffers storing batches ofmultiple commands. The commands for the 3D pipeline 2612 can alsoinclude references to data stored in memory, such as but not limited tovertex and geometry data for the 3D pipeline 2612 and/or image data andmemory objects for the media pipeline 2616. The 3D pipeline 2612 andmedia pipeline 2616 process the commands and data by performingoperations via logic within the respective pipelines or by dispatchingone or more execution threads to a graphics core array 2714.

In various embodiments the 3D pipeline 2612 can execute one or moreshader programs, such as vertex shaders, geometry shaders, pixelshaders, fragment shaders, compute shaders, or other shader programs, byprocessing the instructions and dispatching execution threads to thegraphics core array 2714. The graphics core array 2714 provides aunified block of execution resources. Multi-purpose execution logic(e.g., execution units) within the graphics core array 2714 includessupport for various 3D API shader languages and can execute multiplesimultaneous execution threads associated with multiple shaders.

In some embodiments the graphics core array 2714 also includes executionlogic to perform media functions, such as video and/or image processing.In one embodiment, the execution units additionally includegeneral-purpose logic that is programmable to perform parallel generalpurpose computational operations, in addition to graphics processingoperations. The general purpose logic can perform processing operationsin parallel or in conjunction with general purpose logic within theprocessor core(s) 2407 of FIG. 24 or core 2502A-2502N as in FIG. 25.

Output data generated by threads executing on the graphics core array2714 can output data to memory in a unified return buffer (URB) 2718.The URB 2718 can store data for multiple threads. In some embodimentsthe URB 2718 may be used to send data between different threadsexecuting on the graphics core array 2714. In some embodiments the URB2718 may additionally be used for synchronization between threads on thegraphics core array and fixed function logic within the shared functionlogic 2720.

In some embodiments, graphics core array 2714 is scalable, such that thearray includes a variable number of graphics cores, each having avariable number of execution units based on the target power andperformance level of GPE 2710. In one embodiment the execution resourcesare dynamically scalable, such that execution resources may be enabledor disabled as needed.

The graphics core array 2714 couples with shared function logic 2720that includes multiple resources that are shared between the graphicscores in the graphics core array. The shared functions within the sharedfunction logic 2720 are hardware logic units that provide specializedsupplemental functionality to the graphics core array 2714. In variousembodiments, shared function logic 2720 includes but is not limited tosampler 2721, math 2722, and inter-thread communication (ITC) 2723logic. Additionally, some embodiments implement one or more cache(s)2725 within the shared function logic 2720. A shared function isimplemented where the demand for a given specialized function isinsufficient for inclusion within the graphics core array 2714. Insteada single instantiation of that specialized function is implemented as astand-alone entity in the shared function logic 2720 and shared amongthe execution resources within the graphics core array 2714. The preciseset of functions that are shared between the graphics core array 2714and included within the graphics core array 2714 varies betweenembodiments.

FIG. 28 is a block diagram of another embodiment of a graphics processor2800. Elements of FIG. 28 having the same reference numbers (or names)as the elements of any other figure herein can operate or function inany manner similar to that described elsewhere herein, but are notlimited to such.

In some embodiments, graphics processor 2800 includes a ringinterconnect 2802, a pipeline front-end 2804, a media engine 2837, andgraphics cores 2880A-2880N. In some embodiments, ring interconnect 2802couples the graphics processor to other processing units, includingother graphics processors or one or more general-purpose processorcores. In some embodiments, the graphics processor is one of manyprocessors integrated within a multi-core processing system.

In some embodiments, graphics processor 2800 receives batches ofcommands via ring interconnect 2802. The incoming commands areinterpreted by a command streamer 2803 in the pipeline front-end 2804.In some embodiments, graphics processor 2800 includes scalable executionlogic to perform 3D geometry processing and media processing via thegraphics core(s) 2880A-2880N. For 3D geometry processing commands,command streamer 2803 supplies commands to geometry pipeline 2836. Forat least some media processing commands, command streamer 2803 suppliesthe commands to a video front end 2834, which couples with a mediaengine 2837. In some embodiments, media engine 2837 includes a VideoQuality Engine (VQE) 2830 for video and image post-processing and amulti-format encode/decode (MFX) 2833 engine to providehardware-accelerated media data encode and decode. In some embodiments,geometry pipeline 2836 and media engine 2837 each generate executionthreads for the thread execution resources provided by at least onegraphics core 2880A.

In some embodiments, graphics processor 2800 includes scalable threadexecution resources featuring modular cores 2880A-2880N (sometimesreferred to as core slices), each having multiple sub-cores 2850A-550N,2860A-2860N (sometimes referred to as core sub-slices). In someembodiments, graphics processor 2800 can have any number of graphicscores 2880A through 2880N. In some embodiments, graphics processor 2800includes a graphics core 2880A having at least a first sub-core 2850Aand a second sub-core 2860A. In other embodiments, the graphicsprocessor is a low power processor with a single sub-core (e.g., 2850A).In some embodiments, graphics processor 2800 includes multiple graphicscores 2880A-2880N, each including a set of first sub-cores 2850A-2850Nand a set of second sub-cores 2860A-2860N. Each sub-core in the set offirst sub-cores 2850A-2850N includes at least a first set of executionunits 2852A-2852N and media/texture samplers 2854A-2854N. Each sub-corein the set of second sub-cores 2860A-2860N includes at least a secondset of execution units 2862A-2862N and samplers 2864A-2864N. In someembodiments, each sub-core 2850A-2850N, 2860A-2860N shares a set ofshared resources 2870A-2870N. In some embodiments, the shared resourcesinclude shared cache memory and pixel operation logic. Other sharedresources may also be included in the various embodiments of thegraphics processor.

Additional Exemplary Execution Units

FIG. 29 illustrates thread execution logic 2900 including an array ofprocessing elements employed in some embodiments of a GPE. Elements ofFIG. 29 having the same reference numbers (or names) as the elements ofany other figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such.

In some embodiments, thread execution logic 2900 includes a shaderprocessor 2902, a thread dispatcher 2904, instruction cache 2906, ascalable execution unit array including a plurality of execution units2908A-2908N, a sampler 2910, a data cache 2912, and a data port 2914. Inone embodiment the scalable execution unit array can dynamically scaleby enabling or disabling one or more execution units (e.g., any ofexecution unit 2908A, 2908B, 2908C, 2908D, through 2908N-1 and 2908N)based on the computational requirements of a workload. In one embodimentthe included components are interconnected via an interconnect fabricthat links to each of the components. In some embodiments, threadexecution logic 2900 includes one or more connections to memory, such assystem memory or cache memory, through one or more of instruction cache2906, data port 2914, sampler 2910, and execution units 2908A-2908N. Insome embodiments, each execution unit (e.g. 2908A) is a stand-aloneprogrammable general purpose computational unit that is capable ofexecuting multiple simultaneous hardware threads while processingmultiple data elements in parallel for each thread. In variousembodiments, the array of execution units 2908A-2908N is scalable toinclude any number individual execution units.

In some embodiments, the execution units 2908A-2908N are primarily usedto execute shader programs. A shader processor 2902 can process thevarious shader programs and dispatch execution threads associated withthe shader programs via a thread dispatcher 2904. In one embodiment thethread dispatcher includes logic to arbitrate thread initiation requestsfrom the graphics and media pipelines and instantiate the requestedthreads on one or more execution unit in the execution units2908A-2908N. For example, the geometry pipeline (e.g., 2836 of FIG. 28)can dispatch vertex, tessellation, or geometry shaders to the threadexecution logic 2900 (FIG. 29) for processing. In some embodiments,thread dispatcher 2904 can also process runtime thread spawning requestsfrom the executing shader programs.

In some embodiments, the execution units 2908A-2908N support aninstruction set that includes native support for many standard 3Dgraphics shader instructions, such that shader programs from graphicslibraries (e.g., Direct 3D and OpenGL) are executed with a minimaltranslation. The execution units support vertex and geometry processing(e.g., vertex programs, geometry programs, vertex shaders), pixelprocessing (e.g., pixel shaders, fragment shaders) and general-purposeprocessing (e.g., compute and media shaders). Each of the executionunits 2908A-2908N is capable of multi-issue single instruction multipledata (SIMD) execution and multi-threaded operation enables an efficientexecution environment in the face of higher latency memory accesses.Each hardware thread within each execution unit has a dedicatedhigh-bandwidth register file and associated independent thread-state.Execution is multi-issue per clock to pipelines capable of integer,single and double precision floating point operations, SIMD branchcapability, logical operations, transcendental operations, and othermiscellaneous operations. While waiting for data from memory or one ofthe shared functions, dependency logic within the execution units2908A-2908N causes a waiting thread to sleep until the requested datahas been returned. While the waiting thread is sleeping, hardwareresources may be devoted to processing other threads. For example,during a delay associated with a vertex shader operation, an executionunit can perform operations for a pixel shader, fragment shader, oranother type of shader program, including a different vertex shader.

Each execution unit in execution units 2908A-2908N operates on arrays ofdata elements. The number of data elements is the “execution size,” orthe number of channels for the instruction. An execution channel is alogical unit of execution for data element access, masking, and flowcontrol within instructions. The number of channels may be independentof the number of physical Arithmetic Logic Units (ALUs) or FloatingPoint Units (FPUs) for a particular graphics processor. In someembodiments, execution units 2908A-2908N support integer andfloating-point data types.

The execution unit instruction set includes SIMD instructions. Thevarious data elements can be stored as a packed data type in a registerand the execution unit will process the various elements based on thedata size of the elements. For example, when operating on a 256-bit widevector, the 256 bits of the vector are stored in a register and theexecution unit operates on the vector as four separate 64-bit packeddata elements (Quad-Word (QW) size data elements), eight separate 32-bitpacked data elements (Double Word (DW) size data elements), sixteenseparate 16-bit packed data elements (Word (W) size data elements), orthirty-two separate 8-bit data elements (byte (B) size data elements).However, different vector widths and register sizes are possible.

One or more internal instruction caches (e.g., 2906) are included in thethread execution logic 2900 to cache thread instructions for theexecution units. In some embodiments, one or more data caches (e.g.,2912) are included to cache thread data during thread execution. In someembodiments, a sampler 2910 is included to provide texture sampling for3D operations and media sampling for media operations. In someembodiments, sampler 2910 includes specialized texture or media samplingfunctionality to process texture or media data during the samplingprocess before providing the sampled data to an execution unit.

During execution, the graphics and media pipelines send threadinitiation requests to thread execution logic 2900 via thread spawningand dispatch logic. Once a group of geometric objects has been processedand rasterized into pixel data, pixel processor logic (e.g., pixelshader logic, fragment shader logic, etc.) within the shader processor2902 is invoked to further compute output information and cause resultsto be written to output surfaces (e.g., color buffers, depth buffers,stencil buffers, etc.). In some embodiments, a pixel shader or fragmentshader calculates the values of the various vertex attributes that areto be interpolated across the rasterized object. In some embodiments,pixel processor logic within the shader processor 2902 then executes anapplication programming interface (API)-supplied pixel or fragmentshader program. To execute the shader program, the shader processor 2902dispatches threads to an execution unit (e.g., 2908A) via threaddispatcher 2904. In some embodiments, shader processor 2902 uses texturesampling logic in the sampler 2910 to access texture data in texturemaps stored in memory. Arithmetic operations on the texture data and theinput geometry data compute pixel color data for each geometricfragment, or discards one or more pixels from further processing.

In some embodiments, the data port 2914 provides a memory accessmechanism for the thread execution logic 2900 output processed data tomemory for processing on a graphics processor output pipeline. In someembodiments, the data port 2914 includes or couples to one or more cachememories (e.g., data cache 2912) to cache data for memory access via thedata port.

FIG. 30 is a block diagram illustrating graphics processor instructionformats 3000 according to some embodiments. In one or more embodiment,the graphics processor execution units support an instruction set havinginstructions in multiple formats. The solid lined boxes illustrate thecomponents that are generally included in an execution unit instruction,while the dashed lines include components that are optional or that areonly included in a sub-set of the instructions. In some embodiments,instruction format 3000 described and illustrated aremacro-instructions, in that they are instructions supplied to theexecution unit, as opposed to micro-operations resulting frominstruction decode once the instruction is processed.

In some embodiments, the graphics processor execution units nativelysupport instructions in a 128-bit instruction format 3010. A 64-bitcompacted instruction format 3030 is available for some instructionsbased on the selected instruction, instruction options, and number ofoperands. The native 128-bit instruction format 3010 provides access toall instruction options, while some options and operations arerestricted in the 64-bit format 3030. The native instructions availablein the 64-bit format 3030 vary by embodiment. In some embodiments, theinstruction is compacted in part using a set of index values in an indexfield 3013. The execution unit hardware references a set of compactiontables based on the index values and uses the compaction table outputsto reconstruct a native instruction in the 128-bit instruction format3010.

For each format, instruction opcode 3012 defines the operation that theexecution unit is to perform. The execution units execute eachinstruction in parallel across the multiple data elements of eachoperand. For example, in response to an add instruction the executionunit performs a simultaneous add operation across each color channelrepresenting a texture element or picture element. By default, theexecution unit performs each instruction across all data channels of theoperands. In some embodiments, instruction control field 3014 enablescontrol over certain execution options, such as channels selection(e.g., predication) and data channel order (e.g., swizzle). Forinstructions in the 128-bit instruction format 3010 an exec-size field3016 limits the number of data channels that will be executed inparallel. In some embodiments, exec-size field 3016 is not available foruse in the 64-bit compact instruction format 3030.

Some execution unit instructions have up to three operands including twosource operands, src0 3020, src1 3022, and one destination 3018. In someembodiments, the execution units support dual destination instructions,where one of the destinations is implied. Data manipulation instructionscan have a third source operand (e.g., SRC2 3024), where the instructionopcode 3012 determines the number of source operands. An instruction'slast source operand can be an immediate (e.g., hard-coded) value passedwith the instruction.

In some embodiments, the 128-bit instruction format 3010 includes anaccess/address mode field 3026 specifying, for example, whether directregister addressing mode or indirect register addressing mode is used.When direct register addressing mode is used, the register address ofone or more operands is directly provided by bits in the instruction.

In some embodiments, the 128-bit instruction format 3010 includes anaccess/address mode field 3026, which specifies an address mode and/oran access mode for the instruction. In one embodiment the access mode isused to define a data access alignment for the instruction. Someembodiments support access modes including a 16-byte aligned access modeand a 1-byte aligned access mode, where the byte alignment of the accessmode determines the access alignment of the instruction operands. Forexample, when in a first mode, the instruction may use byte-alignedaddressing for source and destination operands and when in a secondmode, the instruction may use 16-byte-aligned addressing for all sourceand destination operands.

In one embodiment, the address mode portion of the access/address modefield 3026 determines whether the instruction is to use direct orindirect addressing. When direct register addressing mode is used bitsin the instruction directly provide the register address of one or moreoperands. When indirect register addressing mode is used, the registeraddress of one or more operands may be computed based on an addressregister value and an address immediate field in the instruction.

In some embodiments instructions are grouped based on opcode 3012bit-fields to simplify Opcode decode 3040. For an 8-bit opcode, bits 4,5, and 6 allow the execution unit to determine the type of opcode. Theprecise opcode grouping shown is merely an example. In some embodiments,a move and logic opcode group 3042 includes data movement and logicinstructions (e.g., move (mov), compare (cmp)). In some embodiments,move and logic group 3042 shares the five most significant bits (MSB),where move (mov) instructions are in the form of 0000xxxxb and logicinstructions are in the form of 0001xxxxb. A flow control instructiongroup 3044 (e.g., call, jump (jmp)) includes instructions in the form of0010xxxxb (e.g., 0x20). A miscellaneous instruction group 3046 includesa mix of instructions, including synchronization instructions (e.g.,wait, send) in the form of 0011xxxxb (e.g., 0x30). A parallel mathinstruction group 3048 includes component-wise arithmetic instructions(e.g., add, multiply (mul)) in the form of 0100xxxxb (e.g., 0x40). Theparallel math group 3048 performs the arithmetic operations in parallelacross data channels. The vector math group 3050 includes arithmeticinstructions (e.g., dp4) in the form of 0101xxxxb (e.g., 0x50). Thevector math group performs arithmetic such as dot product calculationson vector operands.

Additional Exemplary Graphics Pipeline

FIG. 31 is a block diagram of another embodiment of a graphics processor3100. Elements of FIG. 31 having the same reference numbers (or names)as the elements of any other figure herein can operate or function inany manner similar to that described elsewhere herein, but are notlimited to such.

In some embodiments, graphics processor 3100 includes a graphicspipeline 3120, a media pipeline 3130, a display engine 3140, threadexecution logic 3150, and a render output pipeline 3170. In someembodiments, graphics processor 3100 is a graphics processor within amulti-core processing system that includes one or more general purposeprocessing cores. The graphics processor is controlled by registerwrites to one or more control registers (not shown) or via commandsissued to graphics processor 3100 via a ring interconnect 3102. In someembodiments, ring interconnect 3102 couples graphics processor 3100 toother processing components, such as other graphics processors orgeneral-purpose processors. Commands from ring interconnect 3102 areinterpreted by a command streamer 3103, which supplies instructions toindividual components of graphics pipeline 3120 or media pipeline 3130.

In some embodiments, command streamer 3103 directs the operation of avertex fetcher 3105 that reads vertex data from memory and executesvertex-processing commands provided by command streamer 3103. In someembodiments, vertex fetcher 3105 provides vertex data to a vertex shader3107, which performs coordinate space transformation and lightingoperations to each vertex. In some embodiments, vertex fetcher 3105 andvertex shader 3107 execute vertex-processing instructions by dispatchingexecution threads to execution units 3152A-3152B via a thread dispatcher3131.

In some embodiments, execution units 3152A-3152B are an array of vectorprocessors having an instruction set for performing graphics and mediaoperations. In some embodiments, execution units 3152A-3152B have anattached L1 cache 3151 that is specific for each array or shared betweenthe arrays. The cache can be configured as a data cache, an instructioncache, or a single cache that is partitioned to contain data andinstructions in different partitions.

In some embodiments, graphics pipeline 3120 includes tessellationcomponents to perform hardware-accelerated tessellation of 3D objects.In some embodiments, a programmable hull shader 811 configures thetessellation operations. A programmable domain shader 817 providesback-end evaluation of tessellation output. A tessellator 3113 operatesat the direction of hull shader 3111 and contains special purpose logicto generate a set of detailed geometric objects based on a coarsegeometric model that is provided as input to graphics pipeline 3120. Insome embodiments, if tessellation is not used, tessellation components(e.g., hull shader 3111, tessellator 3113, and domain shader 3117) canbe bypassed.

In some embodiments, complete geometric objects can be processed by ageometry shader 3119 via one or more threads dispatched to executionunits 3152A-3152B, or can proceed directly to the clipper 3129. In someembodiments, the geometry shader operates on entire geometric objects,rather than vertices or patches of vertices as in previous stages of thegraphics pipeline. If the tessellation is disabled the geometry shader3119 receives input from the vertex shader 3107. In some embodiments,geometry shader 3119 is programmable by a geometry shader program toperform geometry tessellation if the tessellation units are disabled.

Before rasterization, a clipper 3129 processes vertex data. The clipper3129 may be a fixed function clipper or a programmable clipper havingclipping and geometry shader functions. In some embodiments, arasterizer and depth test component 3173 in the render output pipeline3170 dispatches pixel shaders to convert the geometric objects intotheir per pixel representations. In some embodiments, pixel shader logicis included in thread execution logic 3150. In some embodiments, anapplication can bypass the rasterizer and depth test component 3173 andaccess un-rasterized vertex data via a stream out unit 3123.

The graphics processor 3100 has an interconnect bus, interconnectfabric, or some other interconnect mechanism that allows data andmessage passing amongst the major components of the processor. In someembodiments, execution units 3152A-3152B and associated cache(s) 3151,texture and media sampler 3154, and texture/sampler cache 3158interconnect via a data port 3156 to perform memory access andcommunicate with render output pipeline components of the processor. Insome embodiments, sampler 3154, caches 3151, 3158 and execution units3152A-3152B each have separate memory access paths.

In some embodiments, render output pipeline 3170 contains a rasterizerand depth test component 3173 that converts vertex-based objects into anassociated pixel-based representation. In some embodiments, therasterizer logic includes a windower/masker unit to perform fixedfunction triangle and line rasterization. An associated render cache3178 and depth cache 3179 are also available in some embodiments. Apixel operations component 3177 performs pixel-based operations on thedata, though in some instances, pixel operations associated with 2Doperations (e.g. bit block image transfers with blending) are performedby the 2D engine 3141, or substituted at display time by the displaycontroller 3143 using overlay display planes. In some embodiments, ashared L3 cache 3175 is available to all graphics components, allowingthe sharing of data without the use of main system memory.

In some embodiments, graphics processor media pipeline 3130 includes amedia engine 3137 and a video front-end 3134. In some embodiments, videofront-end 3134 receives pipeline commands from the command streamer3103. In some embodiments, media pipeline 3130 includes a separatecommand streamer. In some embodiments, video front-end 3134 processesmedia commands before sending the command to the media engine 3137. Insome embodiments, media engine 3137 includes thread spawningfunctionality to spawn threads for dispatch to thread execution logic3150 via thread dispatcher 3131.

In some embodiments, graphics processor 3100 includes a display engine3140. In some embodiments, display engine 3140 is external to processor3100 and couples with the graphics processor via the ring interconnect3102, or some other interconnect bus or fabric. In some embodiments,display engine 3140 includes a 2D engine 3141 and a display controller3143. In some embodiments, display engine 3140 contains special purposelogic capable of operating independently of the 3D pipeline. In someembodiments, display controller 3143 couples with a display device (notshown), which may be a system integrated display device, as in a laptopcomputer, or an external display device attached via a display deviceconnector.

In some embodiments, graphics pipeline 3120 and media pipeline 3130 areconfigurable to perform operations based on multiple graphics and mediaprogramming interfaces and are not specific to any one applicationprogramming interface (API). In some embodiments, driver software forthe graphics processor translates API calls that are specific to aparticular graphics or media library into commands that can be processedby the graphics processor. In some embodiments, support is provided forthe Open Graphics Library (OpenGL), Open Computing Language (OpenCL),and/or Vulkan graphics and compute API, all from the Khronos Group. Insome embodiments, support may also be provided for the Direct3D libraryfrom the Microsoft Corporation. In some embodiments, a combination ofthese libraries may be supported. Support may also be provided for theOpen Source Computer Vision Library (OpenCV). A future API with acompatible 3D pipeline would also be supported if a mapping can be madefrom the pipeline of the future API to the pipeline of the graphicsprocessor.

Graphics Pipeline Programming

FIG. 32A is a block diagram illustrating a graphics processor commandformat 3200 according to some embodiments. FIG. 32B is a block diagramillustrating a graphics processor command sequence 3210 according to anembodiment. The solid lined boxes in FIG. 32A illustrate the componentsthat are generally included in a graphics command while the dashed linesinclude components that are optional or that are only included in asub-set of the graphics commands. The exemplary graphics processorcommand format 3200 of FIG. 32A includes data fields to identify atarget client 3202 of the command, a command operation code (opcode)3204, and the relevant data 3206 for the command. A sub-opcode 3205 anda command size 3208 are also included in some commands.

In some embodiments, client 3202 specifies the client unit of thegraphics device that processes the command data. In some embodiments, agraphics processor command parser examines the client field of eachcommand to condition the further processing of the command and route thecommand data to the appropriate client unit. In some embodiments, thegraphics processor client units include a memory interface unit, arender unit, a 2D unit, a 3D unit, and a media unit. Each client unithas a corresponding processing pipeline that processes the commands.Once the command is received by the client unit, the client unit readsthe opcode 3204 and, if present, sub-opcode 3205 to determine theoperation to perform. The client unit performs the command usinginformation in data field 3206. For some commands an explicit commandsize 3208 is expected to specify the size of the command. In someembodiments, the command parser automatically determines the size of atleast some of the commands based on the command opcode. In someembodiments commands are aligned via multiples of a double word.

The flow diagram in FIG. 32B shows an exemplary graphics processorcommand sequence 3210. In some embodiments, software or firmware of adata processing system that features an embodiment of a graphicsprocessor uses a version of the command sequence shown to set up,execute, and terminate a set of graphics operations. A sample commandsequence is shown and described for purposes of example only asembodiments are not limited to these specific commands or to thiscommand sequence. Moreover, the commands may be issued as batch ofcommands in a command sequence, such that the graphics processor willprocess the sequence of commands in at least partially concurrence.

In some embodiments, the graphics processor command sequence 3210 maybegin with a pipeline flush command 3212 to cause any active graphicspipeline to complete the currently pending commands for the pipeline. Insome embodiments, the 3D pipeline 3222 and the media pipeline 3224 donot operate concurrently. The pipeline flush is performed to cause theactive graphics pipeline to complete any pending commands. In responseto a pipeline flush, the command parser for the graphics processor willpause command processing until the active drawing engines completepending operations and the relevant read caches are invalidated.Optionally, any data in the render cache that is marked ‘dirty’ can beflushed to memory. In some embodiments, pipeline flush command 3212 canbe used for pipeline synchronization or before placing the graphicsprocessor into a low power state.

In some embodiments, a pipeline select command 3213 is used when acommand sequence requires the graphics processor to explicitly switchbetween pipelines. In some embodiments, a pipeline select command 3213is required only once within an execution context before issuingpipeline commands unless the context is to issue commands for bothpipelines. In some embodiments, a pipeline flush command 3212 isrequired immediately before a pipeline switch via the pipeline selectcommand 3213.

In some embodiments, a pipeline control command 3214 configures agraphics pipeline for operation and is used to program the 3D pipeline3222 and the media pipeline 3224. In some embodiments, pipeline controlcommand 3214 configures the pipeline state for the active pipeline. Inone embodiment, the pipeline control command 3214 is used for pipelinesynchronization and to clear data from one or more cache memories withinthe active pipeline before processing a batch of commands.

In some embodiments, commands specific to the return buffer state 3216are used to configure a set of return buffers for the respectivepipelines to write data. Some pipeline operations require theallocation, selection, or configuration of one or more return buffersinto which the operations write intermediate data during processing. Insome embodiments, the graphics processor also uses one or more returnbuffers to store output data and to perform cross thread communication.In some embodiments, the return buffer state 3216 includes selecting thesize and number of return buffers to use for a set of pipelineoperations.

The remaining commands in the command sequence differ based on theactive pipeline for operations. Based on a pipeline determination 3220,the command sequence is tailored to the 3D pipeline 3222 beginning withthe 3D pipeline state 3230 or the media pipeline 3224 beginning at themedia pipeline state 3240.

The commands to configure the 3D pipeline state 3230 include 3D statesetting commands for vertex buffer state, vertex element state, constantcolor state, depth buffer state, and other state variables that are tobe configured before 3D primitive commands are processed. The values ofthese commands are determined at least in part based on the particular3D API in use. In some embodiments, 3D pipeline state 3230 commands arealso able to selectively disable or bypass certain pipeline elements ifthose elements will not be used.

In some embodiments, 3D primitive 3232 command is used to submit 3Dprimitives to be processed by the 3D pipeline. Commands and associatedparameters that are passed to the graphics processor via the 3Dprimitive 3232 command are forwarded to the vertex fetch function in thegraphics pipeline. The vertex fetch function uses the 3D primitive 3232command data to generate vertex data structures. The vertex datastructures are stored in one or more return buffers. In someembodiments, 3D primitive 3232 command is used to perform vertexoperations on 3D primitives via vertex shaders. To process vertexshaders, 3D pipeline 3222 dispatches shader execution threads tographics processor execution units.

In some embodiments, 3D pipeline 3222 is triggered via an execute 3234command or event. In some embodiments, a register write triggers commandexecution. In some embodiments execution is triggered via a ‘go’ or‘kick’ command in the command sequence. In one embodiment, commandexecution is triggered using a pipeline synchronization command to flushthe command sequence through the graphics pipeline. The 3D pipeline willperform geometry processing for the 3D primitives. Once operations arecomplete, the resulting geometric objects are rasterized and the pixelengine colors the resulting pixels. Additional commands to control pixelshading and pixel back end operations may also be included for thoseoperations.

In some embodiments, the graphics processor command sequence 3210follows the media pipeline 3224 path when performing media operations.In general, the specific use and manner of programming for the mediapipeline 3224 depends on the media or compute operations to beperformed. Specific media decode operations may be offloaded to themedia pipeline during media decode. In some embodiments, the mediapipeline can also be bypassed and media decode can be performed in wholeor in part using resources provided by one or more general purposeprocessing cores. In one embodiment, the media pipeline also includeselements for general-purpose graphics processor unit (GPGPU) operations,where the graphics processor is used to perform SIMD vector operationsusing computational shader programs that are not explicitly related tothe rendering of graphics primitives.

In some embodiments, media pipeline 3224 is configured in a similarmanner as the 3D pipeline 3222. A set of commands to configure the mediapipeline state 3240 are dispatched or placed into a command queue beforethe media object commands 3242. In some embodiments, the set of commandsto configure the media pipeline state 3240 include data to configure themedia pipeline elements that will be used to process the media objects.This includes data to configure the video decode and video encode logicwithin the media pipeline, such as encode or decode format. In someembodiments, the commands to configure the media pipeline state 3240also support the use of one or more pointers to “indirect” stateelements that contain a batch of state settings.

In some embodiments, media object commands 3242 supply pointers to mediaobjects for processing by the media pipeline. The media objects includememory buffers containing video data to be processed. In someembodiments, all media pipeline states must be valid before issuing amedia object command 3242. Once the pipeline state is configured andmedia object commands 3242 are queued, the media pipeline 3224 istriggered via an execute command 3244 or an equivalent execute event(e.g., register write). Output from media pipeline 3224 may then be postprocessed by operations provided by the 3D pipeline 3222 or the mediapipeline 3224. In some embodiments, GPGPU operations are configured andexecuted in a similar manner as media operations.

Graphics Software Architecture

FIG. 33 illustrates exemplary graphics software architecture for a dataprocessing system 3300 according to some embodiments. In someembodiments, software architecture includes a 3D graphics application3310, an operating system 3320, and at least one processor 3330. In someembodiments, processor 3330 includes a graphics processor 3332 and oneor more general-purpose processor core(s) 3334. The graphics application3310 and operating system 3320 each execute in the system memory 3350 ofthe data processing system.

In some embodiments, 3D graphics application 3310 contains one or moreshader programs including shader instructions 3312. The shader languageinstructions may be in a high-level shader language, such as the HighLevel Shader Language (HLSL) or the OpenGL Shader Language (GLSL). Theapplication also includes executable instructions 3314 in a machinelanguage suitable for execution by the general-purpose processor core3334. The application also includes graphics objects 3316 defined byvertex data.

In some embodiments, operating system 3320 is a Microsoft® Windows®operating system from the Microsoft Corporation, a proprietary UNIX-likeoperating system, or an open source UNIX-like operating system using avariant of the Linux kernel. The operating system 3320 can support agraphics API 3322 such as the Direct3D API, the OpenGL API, or theVulkan API. When the Direct3D API is in use, the operating system 3320uses a front-end shader compiler 3324 to compile any shader instructions3312 in HLSL into a lower-level shader language. The compilation may bea just-in-time (JIT) compilation or the application can perform shaderpre-compilation. In some embodiments, high-level shaders are compiledinto low-level shaders during the compilation of the 3D graphicsapplication 3310. In some embodiments, the shader instructions 3312 areprovided in an intermediate form, such as a version of the StandardPortable Intermediate Representation (SPIR) used by the Vulkan API.

In some embodiments, user mode graphics driver 3326 contains a back-endshader compiler 3327 to convert the shader instructions 3312 into ahardware specific representation. When the OpenGL API is in use, shaderinstructions 3312 in the GLSL high-level language are passed to a usermode graphics driver 3326 for compilation. In some embodiments, usermode graphics driver 3326 uses operating system kernel mode functions3328 to communicate with a kernel mode graphics driver 3329. In someembodiments, kernel mode graphics driver 3329 communicates with graphicsprocessor 3332 to dispatch commands and instructions.

IP Core Implementations

One or more aspects of at least one embodiment may be implemented byrepresentative code stored on a machine-readable medium which representsand/or defines logic within an integrated circuit such as a processor.For example, the machine-readable medium may include instructions whichrepresent various logic within the processor. When read by a machine,the instructions may cause the machine to fabricate the logic to performthe techniques described herein. Such representations, known as “IPcores,” are reusable units of logic for an integrated circuit that maybe stored on a tangible, machine-readable medium as a hardware modelthat describes the structure of the integrated circuit. The hardwaremodel may be supplied to various customers or manufacturing facilities,which load the hardware model on fabrication machines that manufacturethe integrated circuit. The integrated circuit may be fabricated suchthat the circuit performs operations described in association with anyof the embodiments described herein.

FIG. 34 is a block diagram illustrating an IP core development system3400 that may be used to manufacture an integrated circuit to performoperations according to an embodiment. The IP core development system3400 may be used to generate modular, re-usable designs that can beincorporated into a larger design or used to construct an entireintegrated circuit (e.g., an SOC integrated circuit). A design facility3430 can generate a software simulation 3410 of an IP core design in ahigh level programming language (e.g., C/C++). The software simulation3410 can be used to design, test, and verify the behavior of the IP coreusing a simulation model 3412. The simulation model 3412 may includefunctional, behavioral, and/or timing simulations. A register transferlevel (RTL) design 3415 can then be created or synthesized from thesimulation model 3412. The RTL design 3415 is an abstraction of thebehavior of the integrated circuit that models the flow of digitalsignals between hardware registers, including the associated logicperformed using the modeled digital signals. In addition to an RTLdesign 3415, lower-level designs at the logic level or transistor levelmay also be created, designed, or synthesized. Thus, the particulardetails of the initial design and simulation may vary.

The RTL design 3415 or equivalent may be further synthesized by thedesign facility into a hardware model 3′0, which may be in a hardwaredescription language (HDL), or some other representation of physicaldesign data. The HDL may be further simulated or tested to verify the IPcore design. The IP core design can be stored for delivery to a 3^(rd)party fabrication facility 3465 using non-volatile memory 3440 (e.g.,hard disk, flash memory, or any non-volatile storage medium).Alternatively, the IP core design may be transmitted (e.g., via theInternet) over a wired connection 3450 or wireless connection 3460. Thefabrication facility 3465 may then fabricate an integrated circuit thatis based at least in part on the IP core design. The fabricatedintegrated circuit can be configured to perform operations in accordancewith at least one embodiment described herein.

Exemplary System on a Chip Integrated Circuit

FIG. 35-37 illustrated exemplary integrated circuits and associatedgraphics processors that may be fabricated using one or more IP cores,according to various embodiments described herein. In addition to whatis illustrated, other logic and circuits may be included, includingadditional graphics processors/cores, peripheral interface controllers,or general purpose processor cores.

FIG. 35 is a block diagram illustrating an exemplary system on a chipintegrated circuit 3500 that may be fabricated using one or more IPcores, according to an embodiment. Exemplary integrated circuit 3500includes one or more application processor(s) 3505 (e.g., CPUs), atleast one graphics processor 3510, and may additionally include an imageprocessor 3515 and/or a video processor 3520, any of which may be amodular IP core from the same or multiple different design facilities.Integrated circuit 3500 includes peripheral or bus logic including a USBcontroller 3525, UART controller 3530, an SPI/SDIO controller 3535, andan I²S/I²C controller 3540. Additionally, the integrated circuit caninclude a display device 3545 coupled to one or more of ahigh-definition multimedia interface (HDMI) controller 3550 and a mobileindustry processor interface (MIPI) display interface 3555. Storage maybe provided by a flash memory subsystem 3560 including flash memory anda flash memory controller. Memory interface may be provided via a memorycontroller 3565 for access to SDRAM or SRAM memory devices. Someintegrated circuits additionally include an embedded security engine3570.

FIG. 36 is a block diagram illustrating an exemplary graphics processor3610 of a system on a chip integrated circuit that may be fabricatedusing one or more IP cores, according to an embodiment. Graphicsprocessor 3610 can be a variant of the graphics processor 3610 of FIG.36. Graphics processor 3610 includes a vertex processor 3605 and one ormore fragment processor(s) 3615A-3615N (e.g., 3615A, 3615B, 3615C,3615D, through 3615N-1, and 3615N). Graphics processor 3610 can executedifferent shader programs via separate logic, such that the vertexprocessor 3605 is optimized to execute operations for vertex shaderprograms, while the one or more fragment processor(s) 3615A-3615Nexecute fragment (e.g., pixel) shading operations for fragment or pixelshader programs. The vertex processor 3605 performs the vertexprocessing stage of the 3D graphics pipeline and generates primitivesand vertex data. The fragment processor(s) 3615A-3615N use the primitiveand vertex data generated by the vertex processor 3605 to produce aframebuffer that is displayed on a display device. In one embodiment,the fragment processor(s) 3615A-3615N are optimized to execute fragmentshader programs as provided for in the OpenGL API, which may be used toperform similar operations as a pixel shader program as provided for inthe Direct 3D API.

Graphics processor 3610 additionally includes one or more memorymanagement units (MMUs) 3620A-3620B, cache(s) 3625A-3625B, and circuitinterconnect(s) 3630A-3630B. The one or more MMU(s) 3620A-3620B providefor virtual to physical address mapping for graphics processor 3610,including for the vertex processor 3605 and/or fragment processor(s)3615A-3615N, which may reference vertex or image/texture data stored inmemory, in addition to vertex or image/texture data stored in the one ormore cache(s) 3625A-3625B. In one embodiment the one or more MMU(s)3625A-3625B may be synchronized with other MMUs within the system,including one or more MMUs associated with the one or more applicationprocessor(s) 3505, image processor 3515, and/or video processor 3520 ofFIG. 35, such that each processor 3505-3520 can participate in a sharedor unified virtual memory system. The one or more circuitinterconnect(s) 3630A-3630B enable graphics processor 3610 to interfacewith other IP cores within the SoC, either via an internal bus of theSoC or via a direct connection, according to embodiments.

FIG. 37 is a block diagram illustrating an additional exemplary graphicsprocessor 3710 of a system on a chip integrated circuit that may befabricated using one or more IP cores, according to an embodiment.Graphics processor 3710 can be a variant of the graphics processor 3510of FIG. 35. Graphics processor 3710 includes the one or more MMU(s)3520A-3520B, caches 3525A-3525B, and circuit interconnects 3530A-3530Bof the integrated circuit 3500 of FIG. 35.

Graphics processor 3710 includes one or more shader core(s) 3715A-3715N(e.g., 3715A, 3715B, 3715C, 3715D, 3715E, 3715F, through 3715N-1, and3715N), which provides for a unified shader core architecture in which asingle core or type or core can execute all types of programmable shadercode, including shader program code to implement vertex shaders,fragment shaders, and/or compute shaders. The exact number of shadercores present can vary among embodiments and implementations.Additionally, graphics processor 3710 includes an inter-core taskmanager 3705, which acts as a thread dispatcher to dispatch executionthreads to one or more shader cores 3715A-3715N and a tiling unit 3718to accelerate tiling operations for tile-based rendering, in whichrendering operations for a scene are subdivided in image space, forexample to exploit local spatial coherence within a scene or to optimizeuse of internal caches.

Embodiments described herein provide a logic unit that includes a mergedinteger/floating-point datapath for both multiply-add (e.g., a*b+c) andmultiply-accumulate (e.g., c=c+a*b) operations. In one embodiment anaddend for the add operation is based on an accumulation of previousoperations. In one embodiment the integer datapath of the logic unit ismerged into a floating-point datapath that has addend alignmentoperation in parallel with a multiply operation. In one embodiment theinteger datapath is merged into a floating-point datapath that hasaddend alignment operation after the multiply operation. Themultiply-add and multiply-accumulate datapaths described herein may besingle-cycle or multi-cycle.

In one embodiment, during a two-cycle floating-pointmultiply-accumulate, the logic unit does not compare the mantissas atthe beginning of second stage (e.g., adder stage). Instead, the logicunit precomputes a larger (or smaller) mantissa based on an accumulatorexponent from the second stage and multiplier output computed during thefirst stage.

In one embodiment the accumulator or addend mantissa bit-width is largerthan the mantissa bit-widths of the multiplier inputs. In one embodimentthe integer operations are mapped on to a floating-point unit. Some ofthe integer operations are also mapped onto existing exponent circuitsin addition to the mantissa circuits of a floating-point unit. In oneembodiment the logic units described herein include a multiplier unitand an adder unit that are shared between floating-point and integeroperations and are used to perform both floating-point and integeroperations.

The following clauses and/or examples pertain to specific embodiments orexamples thereof. Specifics in the examples may be used anywhere in oneor more embodiments. The various features of the different embodimentsor examples may be variously combined with some features included andothers excluded to suit a variety of different applications. Examplesmay include subject matter such as a method, means for performing actsof the method, at least one machine-readable medium includinginstructions that, when performed by a machine cause the machine toperform acts of the method, or of an apparatus or system according toembodiments and examples described herein. Various components can be ameans for performing the operations or functions described.

One embodiment provides for a machine-learning hardware acceleratorcomprising a compute unit having an adder and a multiplier that areshared between integer data path and a floating-point datapath, theupper bits of input operands to the multiplier to be gated duringfloating-point operation. In one embodiment the adder and the multiplierare configurable to perform floating-point operation and integeroperation. In one embodiment the compute unit is to perform amultiply-add operation via the multiplier and the adder. In oneembodiment the compute unit accepts at least two input operands. Oneembodiment provides for a compute unit to perform a multiply-accumulateoperation using two-input operands and an accumulated value. Oneembodiment provides for a compute unit to perform a multiply-addoperation using three input operands. In one embodiment the compute unitis to perform a multiply-accumulate operation or a multiply-addoperation within a single cycle. In one embodiment the compute unit isto perform a two-cycle multiply-add operation or a two-cyclemultiply-accumulate operation. In one embodiment the multiplier withinthe compute unit is to produce an output during a first cycle and theadder is to produce an output during a second cycle. In one embodimentthe compute unit is to perform a two-cycle multiply-accumulate operationin which the first cycle is associated with a first logic stage, thesecond cycle is associated with a second logic stage, and the computeunit includes an exponent unit to precompute a larger mantissa andalignment shift for the second stage via an accumulated output of aprevious cycle of the second stage and multiplier output from the firststage.

In one embodiment the integer datapath is merged into the floating-pointdatapath having an addend alignment operation in parallel with themultiply operation. In one embodiment the integer datapath is mergedinto the floating-point datapath having an addend alignment operationafter the multiply operation. The compute unit can have a mode input toswitch the compute unit between integer operation and floating-pointoperation. In one embodiment the compute unit is configurable for an 8.8fixed-point input and a 16.0 fixed-point output.

One embodiment provides for a data processing system comprising anon-transitory machine-readable medium to store instructions forexecution by one or more processors of the data processing system; and ageneral-purpose graphics processing unit comprising a machine-learninghardware accelerator and a dynamic precision compute unit, themachine-learning hardware accelerator including hardware logic toperform multiple machine-learning compute operations in response to asingle instruction. In one embodiment the dynamic precision compute unitis switchable between integer operation and floating-point operation. Inone embodiment the dynamic precision compute unit includes an integerdatapath and floating-point datapath that share a multiplier and anadder, where the multiplier is to perform a multiply operation for theinteger datapath and the floating-point datapath. In one embodiment thefloating-point datapath includes an addend alignment operation performedin parallel with the multiply operation. In one embodiment thefloating-point datapath includes an addend alignment operation performedafter the multiply operation. In one embodiment the dynamic precisioncompute unit is configured for a single-cycle fused multiply-addoperation or a two-cycle fused multiply-accumulate operation.

One embodiment provides for a method of accelerating machine-learningoperations, the method comprising fetching and decoding a singleinstruction to perform a combined multiply and add operation on a set ofoperands; issuing the single instruction for execution by a dynamicallyconfigurable compute unit; configuring one of more logic units of thecompute unit to perform operations at the precision and data-type of theset of operands; and executing at least a portion of the singleinstruction at the dynamically configurable compute unit to generate andoutput based on the multiply and add operation.

One embodiment provides for a graphics processing unit to acceleratemachine-learning operations, the graphics processing unit comprising amultiprocessor having a single instruction, multiple thread (SIMT)architecture, the multiprocessor to execute at least one singleinstruction and a first compute unit included within the multiprocessor,the at least one single instruction to cause the first compute unit toperform a two-dimensional matrix multiply and accumulate operation,wherein to perform the two-dimensional matrix multiply and accumulateoperation includes to compute a 32-bit intermediate product of 16-bitoperands and to compute a 32-bit sum based on the 32-bit intermediateproduct. In one embodiment, the multiprocessor is to execute parallelthreads of a thread group, each thread of the thread group havingindependent thread state. In one embodiment, the multiprocessor includesa scheduler to schedule the parallel threads of the thread group tomultiple compute units within the multiprocessor. In one embedment, themultiple compute units of graphics processing unit include a secondcompute unit to perform an integer operation, the scheduler to schedulea floating-point operation to the first compute unit and an integeroperation to the second compute unit. The multiprocessor cansimultaneously execute a floating-point operation on the first computeunit and an integer operation on the second compute unit. The firstcompute unit can compute the 32-bit intermediate product from two ormore 16-bit operands of the at least one single instruction. The firstcompute unit can compute a 16-bit sum based on the 32-bit intermediateproduct.

One embodiment provides for a data processing system comprising anon-transitory machine-readable medium to store instructions forexecution; a graphics processing unit to accelerate machine-learningoperations, the graphics processing unit including a multiprocessorhaving a single instruction, multiple thread (SIMT) architecture, themultiprocessor to execute at least one single instruction; and a firstcompute unit included within the multiprocessor, the at least one singleinstruction to cause the first compute unit to perform a two-dimensionalmatrix multiply and accumulate operation, wherein to perform thetwo-dimensional matrix multiply and accumulate operation includes tocompute a 32-bit intermediate product of 16-bit operands and to computea 32-bit sum based on the 32-bit intermediate product.

One embodiment provides for a method of accelerating a machine-learningoperation, the method comprising decoding a single instruction on agraphics processing unit (GPU), the GPU having a single instruction,multiple thread (SIMT) architecture; executing the single instructionvia a multiprocessor within the GPU; and in response to executing thesingle instruction via the multiprocessor, performing a two-dimensionalmatrix multiply and accumulate operation on a first compute unit of themultiprocessor, wherein performing the two-dimensional matrix multiplyand accumulate operation includes computing a 32-bit intermediateproduct of 16-bit operands and computing a 32-bit sum based on the32-bit intermediate product. In one embodiment, the method additionallycomprises executing parallel threads of a thread group, each thread ofthe thread group having independent thread state and scheduling theparallel threads of the thread group to multiple compute units withinthe multiprocessor. In one embodiment, the method additionally comprisesscheduling a floating-point operation to the first compute unit and aninteger operation to a second compute unit; and performing the integeroperation via a second compute unit within the multiprocessor. In oneembodiment, the method additionally comprises simultaneously executing afloating-point operation on the first compute unit and an integeroperation on the second compute unit. In one embodiment the methodadditionally comprises computing the 32-bit intermediate product fromtwo or more 16-bit operands of the at least one single instruction andcomputing; and computing a 16-bit sum based on the 32-bit intermediateproduct.

One embodiment provides for an apparatus comprising an interconnectfabric, a memory interface coupled to the interconnect fabric, aninput/output, IO, unit coupled to the interconnect fabric, an array ofmultiprocessors coupled to the interconnect fabric, a multiprocessor inthe array of multiprocessors to execute a mixed-precision instruction inparallel across multiple threads, where the multiprocessor in the arrayof multiprocessors comprises a plurality of registers to store packedfloating-point operand values and execution circuitry to execute one ormore of the mixed-precision instructions to perform a fusedmultiply-accumulate operation, the execution circuitry comprising a16-bit multiplier to multiply a first 16-bit floating point source valueand a second 16-bit floating point source value to generate anintermediate result and a 32-bit accumulator to add the intermediateresult to an accumulated floating point value to generate a newaccumulation result.

The embodiments described herein refer to specific configurations ofhardware, such as application specific integrated circuits (ASICs),configured to perform certain operations or having a predeterminedfunctionality. Such electronic devices typically include a set of one ormore processors coupled to one or more other components, such as one ormore storage devices (non-transitory machine-readable storage media),user input/output devices (e.g., a keyboard, a touchscreen, and/or adisplay), and network connections. The coupling of the set of processorsand other components is typically through one or more busses and bridges(also termed as bus controllers). The storage device and signalscarrying the network traffic respectively represent one or moremachine-readable storage media and machine-readable communication media.Thus, the storage devices of a given electronic device typically storecode and/or data for execution on the set of one or more processors ofthat electronic device.

One embodiment provides for an apparatus comprising an interconnectfabric, a memory interface coupled to the interconnect fabric, aninput/output, IO, unit coupled to the interconnect fabric, an array ofmultiprocessors coupled to the interconnect fabric, a multiprocessor inthe array of multiprocessors comprising a plurality of registers tostore packed floating-point and packed integer operand values including32-bit floating-point values, 16-bit floating-point values, and 8-bitinteger values and a decoder to decode a plurality of mixed-precisionfused multiply-accumulate (FMA) instructions including a first FMAinstruction indicating N 16-bit floating-point source operands and a32-bit floating-point source operand, and a second FMA instructionindicating 2N 8-bit integer source operands and a 32-bit integer sourceoperand, and parallel multiplication circuitry to perform N/2 parallel16-bit floating-point multiplications responsive to the first FMAinstruction to produce N/2 floating-point products, perform N parallel8-bit integer multiplications responsive to the second FMA instructionto produce N integer products, accumulation circuitry to add the N/2floating point products to the 32-bit floating-point source operandresponsive to the first FMA instruction to generate an accumulated32-bit floating-point result, and add the N integer products to the32-bit integer source operand responsive to the second FMA instructionto generate an accumulated 32-bit integer result.

One embodiment provides an apparatus comprising a memory interface andan array of multiprocessors coupled to the memory interface. At leastone multiprocessor in the array of multiprocessors is configured toexecute a fused multiply-add instruction in parallel across multiplethreads. The at least one multiprocessor comprises a register file tostore data and execution circuitry coupled to the register file. Theexecution circuitry is configured to execute the fused multiply-addinstruction to generate a multidimensional result matrix. The executioncircuitry includes hardware logic to convert a first plurality of dataelements of a first multidimensional source matrix and a secondplurality of data elements of a second multidimensional source matrixfrom a 32-bit floating point data format to a reduced precision floatingpoint format having a 1-bit sign, an 8-bit exponent, and a mantissa, themantissa of the reduced precision floating point format having fewerbits than a mantissa of the 32-bit floating point data format. Theexecution circuitry additionally includes a plurality of multiply-addcircuits to perform parallel fused multiply-add operations to multiplythe first plurality of data elements in the reduced precision floatingpoint format by corresponding data elements of the second plurality ofdata elements in the reduced precision floating point format to generatea plurality of products and to add the plurality of products tocorresponding 32-bit floating point values to generate corresponding32-bit floating point data elements of the multidimensional resultmatrix.

In a further embodiment, the mantissa of the reduced precision floatingpoint format comprises a 7-bit mantissa and the fused multiply-addinstruction comprises a first operand to identify the first plurality ofdata elements and a second operand to identify the second plurality ofdata elements. The first operand can identify the first plurality ofdata elements in a first one or more registers of the register file andthe second operand can identify the second plurality of data elements ina second one or more registers of the register file.

In a further embodiment the apparatus comprises an instruction fetchunit to fetch the fused multiply-add instruction, a decoder to decodethe fused multiply-add instruction to generate parallel multiply-addoperations to be performed across the multiple threads, and a schedulerto schedule the parallel multiply-add operations for execution by theexecution circuitry. The execution circuitry can include a plurality ofarithmetic logic units (ALUs). The apparatus can additionally include aninterconnect fabric to couple the array of multiprocessors to the memoryinterface and an input/output (TO) interface coupled to the interconnectfabric. The apparatus can additionally include a shared cache or localmemory shared by the plurality of multiply-add circuits.

In one embodiment, the apparatus includes a local shared memory to storea first plurality of data elements of a first multidimensional sourcematrix and a second plurality of data elements of a secondmultidimensional source matrix in a reduced precision floating pointformat comprising a 1-bit sign, a 7-bit mantissa and an 8-bit exponent.

One embodiment provides a system including an apparatus describedherein.

One embodiment provides a method comprising communicatively coupling anarray of multiprocessors to a memory interface, at least onemultiprocessor in the array of multiprocessors to execute a fusedmultiply-add instruction in parallel across multiple threads, the atleast one multiprocessor comprising a register file to store data andexecution circuitry coupled to the register file and executing the fusedmultiply-add instruction by the execution circuitry to generate amultidimensional result matrix. The executing can include converting afirst plurality of data elements of a first multidimensional sourcematrix and a second plurality of data elements of a secondmultidimensional source matrix from a 32-bit floating point data formatto a reduced precision floating point format having a 1-bit sign, an8-bit exponent, and a mantissa, the mantissa of the reduced precisionfloating point format having fewer bits than a mantissa of the 32-bitfloating point data format and performing parallel fused multiply-addoperations on a plurality of multiply-add circuits to multiply the firstplurality of data elements in the reduced precision floating pointformat by corresponding data elements of the second plurality of dataelements in the reduced precision floating point format to generate aplurality of products, and to add the plurality of products tocorresponding 32-bit floating point values to generate corresponding32-bit floating point data elements of the multidimensional resultmatrix. The mantissa of the reduced precision floating point format cancomprise a 7-bit mantissa and the fused multiply-add instruction cancomprise a first operand to identify the first plurality of dataelements and a second operand to identify the second plurality of dataelements. The first operand can identify the first plurality of dataelements in a first one or more registers of the register file and thesecond operand identifies the second plurality of data elements in asecond one or more registers of the register file. The method can beperformed on a system or apparatus as described herein, and canadditionally include fetching the fused multiply-add instruction,decoding the fused multiply-add instruction to generate parallelmultiply-add operations to be performed across the multiple threads, andscheduling the parallel multiply-add operations for execution by theexecution circuitry, where the execution circuitry includes a pluralityof ALUs. The method further comprises communicatively coupling the arrayof multiprocessors to the memory interface over an interconnect fabric,communicatively coupling an input/output (TO) interface to theinterconnect fabric, and sharing a cache or local memory between theplurality of multiply-add circuits.

One embodiment provides for a graphics processing unit (GPU) comprisinga plurality of memory controllers, cache memory coupled with theplurality of memory controllers, and a graphics multiprocessor coupledwith the cache memory and the plurality of memory controllers. Thegraphics multiprocessor has a single instruction, multiple thread (SIMT)architecture and includes a register file and circuitry coupled with theregister file. The circuitry includes a first core to perform a mixedprecision matrix operation and a second core to perform, in response toa single instruction, multiple compute operations. The multiple computeoperations include a first operation to perform a fused multiply-add anda second operation to apply a rectified linear unit function to a resultof the first operation.

One embodiment provides a graphics processor comprising a memorycontroller and a graphics processing resource coupled with the memorycontroller. The graphics processing resource includes first circuitryconfigured to execute an instruction to perform a matrix operation onfirst input including weight data and second input including inputactivation data, generate intermediate data based on a result of thematrix operation, quantize the intermediate data to a floating-pointformat determined based on a statistical distribution of first outputdata, and output, as second output data, quantized intermediate data ina determined floating-point format. In one embodiment, the matrixoperation associated is matrix multiply operation and the statisticaldistribution of the first output data includes a probability density ofthe first output data. In one embodiment, the first output data includesoutput generated for a first layer of a neural network and the secondoutput data includes output generated for a second layer of a neuralnetwork. The graphics processor additionally includes second circuitryto determine the statistical distribution of the first output data. Thememory controller can include the second circuitry and use the secondcircuitry to determine the statistical distribution of the first outputdata during a write of the first output data to memory. The firstcircuitry can be configured to request a write of the second output datato memory via the memory controller and the memory controller isconfigured to determine, via the second circuitry, the statisticaldistribution of the second output data during the write of the secondoutput data to memory.

One embodiment provides a method to perform operations of the graphicsprocessor described above. A further embodiment provides a dataprocessing system that includes the graphics processor described above.

Of course, one or more parts of an embodiment may be implemented usingdifferent combinations of software, firmware, and/or hardware.Throughout this detailed description, for the purposes of explanation,numerous specific details were set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however, toone skilled in the art that the embodiments may be practiced withoutsome of these specific details. In certain instances, well-knownstructures and functions were not described in elaborate detail to avoidobscuring the inventive subject matter of the embodiments. Accordingly,the scope and spirit of the invention should be judged in terms of theclaims that follow.

1-20. (canceled)
 21. A graphics processor comprising: a memorycontroller; and a graphics multiprocessor coupled with the memorycontroller, the graphics multiprocessor including first circuitryconfigured to: execute an instruction to perform a matrix operation onfirst input and second input; generate intermediate data based on aresult of the matrix operation; convert the intermediate data to afloating-point format determined based on statistics associated withfirst output data; and output, as second output data, convertedintermediate data in a determined floating-point format.
 22. Thegraphics processor as in claim 21, wherein the matrix operation includesa matrix multiply operation.
 23. The graphics processor as in claim 21,wherein the first circuitry is configured to generate the intermediatedata in one of a first plurality of floating-point formats.
 24. Thegraphics processor as in claim 23, wherein the first plurality offloating-point formats includes a 16-bit format and a 32-bit format. 25.The graphics processor as in claim 21, wherein the first circuitry isconfigured to convert the intermediate data to one of a second pluralityof floating-point formats.
 26. The graphics processor as in claim 25,wherein the second plurality of floating-point formats includes multiplefloating-point formats having a same number of bits and a differentnumber of exponent bits.
 27. The graphics processor as in claim 26,wherein the multiple floating-point formats have a different number ofmantissa bits.
 28. The graphics processor as in claim 21, wherein thefirst output data includes output generated for a first layer of aneural network.
 29. The graphics processor as in claim 28, wherein thesecond output data includes output generated for a second layer of aneural network.
 30. The graphics processor as in claim 29, wherein thefirst output data includes data generated by the first circuitry priorto generation of the intermediate data.
 31. The graphics processor as inclaim 21, wherein the statistics associated with the first output dataincludes a dynamic range of the first output data.
 32. The graphicsprocessor as in claim 21, further comprising second circuitry todetermine statistics associated with first output data.
 33. The graphicsprocessor as in claim 32, wherein the memory controller includes thesecond circuitry.
 34. The graphics processor as in claim 33, wherein thememory controller is configured to determine, via the second circuitry,the statistics associated with first output data during a write of thefirst output data to memory.
 35. The graphics processor as in claim 34,wherein the first circuitry is configured to request a write of thesecond output data to memory via the memory controller.
 36. The graphicsprocessor as in claim 35, wherein the memory controller is configured todetermine, via the second circuitry, statistics associated with thesecond output data during the write of the second output data to memory.37. A method comprising: executing an instruction on a graphicsprocessor to perform a matrix operation on first input and second input;generating intermediate data based on a result of the matrix operation;converting the intermediate data to a floating-point format determinedbased on statistics associated with first output data; and outputting,as second output data, converted intermediate data in a determinedfloating-point format.
 38. The method as in claim 37, furthercomprising: generating the intermediate data in one of a first pluralityof floating-point formats, wherein the first plurality of floating-pointformats includes a 16-bit format and a 32-bit format; and converting theintermediate data to one of a second plurality of floating-pointformats, wherein the second plurality of floating-point formats includesmultiple floating-point formats having a same number of bits and adifferent number of exponent bits.
 39. The method as in claim 38,wherein the multiple floating-point formats have a different number ofmantissa bits.
 40. A data processing system comprising: a memory deviceto store an instruction; and a graphics processor coupled with thememory device and configured to execute the instruction, the graphicsprocessor including a memory controller and a graphics multiprocessorcoupled with the memory controller, the graphics processor includingcircuitry configured to: execute an instruction to perform a matrixoperation on first input and second input; generate intermediate databased on a result of the matrix operation; convert the intermediate datato a floating-point format determined based on statistics associatedwith first output data; and output, as second output data, convertedintermediate data in a determined floating-point format.
 41. The dataprocessing system as in claim 40, wherein the matrix operation includesa matrix multiply operation, the circuitry is configured to generate theintermediate data in one of a first plurality of floating-point formats,and the first plurality of floating-point formats includes a 16-bitformat and a 32-bit format.
 42. The data processing system as in claim40, wherein the circuitry is configured to convert the intermediate datato one of a second plurality of floating-point formats, the secondplurality of floating-point formats including multiple floating-pointformats having a same number of bits and a different number of exponentbits.
 43. The data processing system as in claim 42, wherein themultiple floating-point formats have a different number of mantissabits.
 44. The data processing system as in claim 40, the graphicsprocessor including circuitry configured to determine statisticsassociated with first output data.
 45. The data processing system as inclaim 44, wherein the circuitry configured to determine statisticsassociated with first output data is included in the memory controllerof the graphics processor.